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Collaborative systems for large scale citizen participation

William Aboucaya

► To cite this version:

William Aboucaya. Collaborative systems for large scale citizen participation. Software Engineering [cs.SE]. Sorbonne Université, 2023. English. NNT: 2023SORUS461 . tel-04401421

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SORBONNE UNIVERSITÉ

ÉCOLE DOCTORALE 130
INRIA Paris – Équipe projet MiMove

THÈSE

pour obtenir le grade de
DOCTEUR DE L'UNIVERSITÉ SORBONNE UNIVERSITÉ

Discipline : Informatique

Présentée et soutenue par :

William ABOUCAYA

le : 30 novembre 2023

Collaborative systems for large-scale online
citizen participation

Sous la direction de :

M. Nikolaos GEORGANTAS – Directeur de thèse, INRIA Paris

M. Rafael ANGARITA – Co-encadrant, Université Paris Nanterre | LIP6 Sorbonne Université

Mme Valérie ISSARNY – Ancienne directrice de thèse, INRIA Paris

Rapporteurs :

Mme Claudia IGNAT – Chargée de Recherche HDR, INRIA Nancy-Grand Est

M. Christophe PRIEUR - Professeur, LISIS Université Gustave Eiffel

Présidente du jury :

Mme Vanda LUENGO – Professeure, LIP6 Sorbonne Université

Membres du jury :

Mme Sihem AMER-YAHIA - Professeure, CNRS | Université Grenoble Alpes

M. Iván CANTADOR – Associate Professor, Universidad Autónoma de Madrid

M. Nicolas MAUDET – Professeur, LIP6 Sorbonne Université
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# Abstract

Online participatory platforms have become a common means to involve citizens in public decision-making, allowing for participation at a larger scale than their offline counterparts, both in the number of participants and in the geographical distribution. However, the term “participatory platform” covers a wide range of extremely different systems, implying differences in the problems encountered by platforms administrators and contributors. More precisely, such platforms face specific issues when they aim at allowing citizens to collaborate to produce common contributions or when the number of contributors involved becomes particularly high. This Ph.D. research aims at identifying issues in contemporary online citizen participation platforms and proposing technical means to create participatory platforms more collaborative and suitable for large-scale online participation. My thesis is mainly based on previous works produced in the Computer-Supported Collaborative Work (CSCW) and Natural Language Processing (NLP) fields of computer science research.

The contributions of this thesis are: the identification of flaws in a specific citizen participation platform and the recommendation of platform design-oriented alternatives to solve them; the representation of a participatory platform as a knowledge graph and its enrichment using a preexisting external knowledge base; the identification of the different objectives motivating the creation of participatory platforms and of the different types of features for interaction implemented based on a series of interviews; the conception and implementation of a Natural Language Inference-based method to reduce issues faced by online citizen participation when the number of contributors becomes particularly high.
Résumé

Les plates-formes participatives en ligne sont devenues un moyen courant d'impliquer les citoyens dans la prise de décision publique, permettant une participation à plus grande échelle que leurs homologues hors ligne, à la fois en termes de nombre de participants et de répartition géographique. Cependant, le terme "plateforme participative" recouvre un large éventail de systèmes extrêmement différents, ce qui implique des différences dans les problèmes rencontrés par les administrateurs et les contributeurs des plateformes. Plus précisément, ces plateformes font face à des problèmes spécifiques lorsqu'elles visent à permettre aux citoyens de collaborer pour produire des contributions communes ou lorsque le nombre de contributeurs impliqués devient particulièrement élevé. Cette recherche doctorale vise à identifier les problèmes des plateformes de participation citoyenne contemporaines et à proposer des moyens techniques pour créer des plateformes participatives plus collaboratives et adaptées à une participation à grande échelle. Ma thèse s'appuie principalement sur des travaux antérieurs réalisés dans les champs de la recherche en informatique que sont le travail collaboratif assisté par ordinateur (CSCW) et le traitement du langage naturel (NLP).

Les contributions de cette thèse sont : l'identification des biais d'une plateforme participative spécifique et la recommandation d'alternatives centrées sur la conception de la plateforme pour les résoudre ; la représentation d'une plateforme participative sous la forme d'un graphe de connaissances (*knowledge graph*) et son enrichissement à partir d'une base de connaissances externe préexistantes ; l'identification des différents objectifs motivant la création de plateformes participatives et des différents types de fonctionnalités d'interaction mises en œuvre à partir d'une série d'entretiens ; la conception et la mise en œuvre d'une méthode basée sur l'inférence en langage naturel pour réduire les problèmes rencontrés par la participation citoyenne en ligne lorsque le nombre de contributeurs devient particulièrement élevé.
Acknowledgments

The last years of my Ph.D. thesis have come to an end. They have been an occasion
for me to focus on a fascinating topic and I feel deeply grateful for everything I
learned both academically and non academically. These years have also been an
occasion for me to meet wonderful people without whom this work wouldn't be the
same. First, I want to thank my Ph.D. supervisor Rafael Angarita for his support
and guidance during these years. Thank you for giving me the opportunity to work
with you and for every time our meetings have put me back on the right track.

Then, I want to thank my Ph.D. advisor Nikolaos Georgantas who accepted to
supervise the last year of my doctorate. Thank you for welcoming me to the MiMove
team and for giving me helpful advice during the last four years. I also want to thank
the researchers Claudia Ignat and Christophe Prieur for accepting to review my
thesis, and Sihem Amer-Yahia, Iván Cantador, Vanda Luengo and Nicolas Maudet
for their participation in the jury for my Ph.D. defense. This acknowledgment
extends to Julia Lawall, who accepted to be part of my annual evaluation committee.

Many thanks to my former and current colleagues in the MiMove team, Shahin
Abdoul Soukour, Maroua Bahri, Zakaria Benomar, Lior Diler, Yifan Du, Nathalie
Gaudechoux, Meriem Guemair, Diana Marino Duarte, Patient Ntumba, Françoise
Sailhan and Haidong Zhao. Thank you for your support and for bringing joy onto
our floor. I also want to thank Oana Balalau and Sonia Guehis with whom I had
the chance to work on recent research works. Thank you for your great advice, your
patience and for the energy you brought to our weekly meetings.

This work wouldn't have been possible without the support and love of my
wonderful family, my mother Cécile, my sister Ilana and my late father Georges.
Thank you for giving me the chance of being where I stand today and for filling me
with joy and hope. I also want to thank my fantastic friends without whom my life
during these four years and before wouldn't have been the same. I would like to
express my heartfelt gratitude to Stanley Duplan and Prune Krotoff, whose presence
when my doctoral project was accepted filled me with happiness, Anne Labatut for
her continuous support during these years, and Morgane Leclerc, Camille Parrau
and Hugo Plassais for giving me the confidence to apply for a Ph.D. I also thank
my friends Fanny Eberlé, Enzo Gaccio and Elsa Goudenege for their support and
encouragements during these four years and before.

Finally, I want to express my sincere gratitude to my first Ph.D. advisor Valérie
Issary who supervised the first three years of my doctorate. Thank you for every-
thing you taught me, for your guidance and for your support during these three
years. Most importantly, thank you for your energy, for your humor and for your
determination. It has been a true honor and pleasure to work with you.
# Résumé étendu

## 1 Introduction

Ces dernières années, la participation citoyenne en ligne, ainsi que d'autres sujets inclus dans le champ des technologies civiques (civic tech), est devenue de plus en plus fréquemment utilisée avec pour but d'améliorer l'intégration des citoyens dans la prise de décisions publiques. La participation citoyenne en ligne peut prendre des formes diverses, telles que le budget participatif [71, 128], la collecte et la publication de données ouvertes citoyennes [28, 94] ou la consultation citoyenne [83, 148]. De plus, des plateformes participatives ont déjà été utilisées pour de nombreux sujets, par exemple la planification du développement de l'espace public [176], la transformation numérique d'un pays [91] ou la lutte et l'adaptation au changement climatique [63, 133]. Cependant, nous considérons que deux constantes peuvent être identifiées : une plateforme en ligne sur laquelle les citoyens sont amenés à produire et publier des contributions, et une entité organisatrice (par exemple, une mairie, une université) que nous appelons ici "institution" et qui initie le processus participatif. Dans cette thèse, ces éléments sont considérés comme constitutifs de la participation citoyenne en ligne.

L'objectif de cette thèse est de résoudre des problèmes auxquels sont confrontés les citoyens et les administrateurs de plateformes dans le contexte de la participation citoyenne en ligne. Certains de ces problèmes ont été identifiés précédemment par d'autres chercheurs [38, 75, 132] et d'autres sont identifiés dans les Chapitres 3 et 4 de cette thèse. Plus spécifiquement, nous cherchons à réduire la surcharge d'information à laquelle sont confrontés à la fois les citoyens et les administrateurs de plateformes, ainsi qu'à faciliter la collaboration entre les citoyens dans un contexte de participation en ligne à grande échelle. Nous nous concentrons sur la participation citoyenne à grande échelle parce que nous considérons que la scalabilité est l'un des principaux avantages de la participation en ligne par rapport à la participation en personne. Nous pouvons citer l'exemple que donne Robert Dahl en 1998, relevant le fait que donner à chaque citoyen d'une ville de 10.000 citoyens 10 minutes d'expression publique demanderait 200 jours à un rythme de 8 heures de discussion par jour [48]. Bien que ce problème soit particulièrement pertinent pour la participation en personne, il est largement réduit par la possibilité offerte à chacun d'exprimer ou de lire l'expression des autres à n'importe quel moment et à n'importe quel endroit. Par conséquent, les plateformes en ligne permettent à la participation citoyenne d'exister à de multiples échelles, depuis un conseil de quartier [146] à des initiatives impliquant plusieurs pays [20].

Pour répondre aux problèmes identifiés, nos contributions se basent sur des travaux pré-existants dans les champs de recherche en Interaction Humain-Machine (IHM), Travail Collaboratif Assisté par Ordinateur (TCAO) et Traitement Automatique de la Information.
tique du Langage (TAL). Nous proposons de répondre aux deux questions suivantes :

1. Quels sont les problèmes rencontrés par les citoyens et les administrateurs de plateformes dans le contexte de la participation citoyenne en ligne ?<fnref n="1" />

2. À partir de l'état actuel des connaissances en informatique, comment pouvons-nous réduire les problèmes identifiés afin de créer des plateformes participatives viables à grande échelle et permettant davantage de collaboration ?

## 1.1 Contributions

Les contributions de cette thèse sont concentrées autour de trois approches différentes pour améliorer la collaboration dans le cadre de la participation citoyenne à grande échelle. Nous dédions un chapitre de cette thèse à chacune de ces contributions. Leurs objectifs sont d'identifier ou de résoudre divers problèmes rencontrés par la participation citoyenne à grande échelle en s'appuyant sur différents champs de la recherche en informatique.

### Étude d'une plateforme participative existante
La première contribution de cette thèse est une étude d'une consultation citoyenne spécifique autour du projet de loi République Numérique. Nous étudions les interactions entre les contributions et les contributeurs de cette consultation afin d'identifier des failles dans le design de la plateforme. Nous proposons des recommandations de modifications de l'interface utilisateur et du design de la plateforme afin de réduire les biais engendrés par les failles identifiées.

De plus, nous proposons également une représentation des consultations citoyennes en ligne basée sur un graphe de connaissances. Cette représentation nous permet d'associer les propositions soumises dans une consultation à différents sujets et ressources issues de la base de connaissances DBpedia. Nous appliquons cette représentation à la consultation République Numérique afin d'améliorer les possibilités d'identification de contributions pertinentes pour un citoyen et d'aider les administrateurs de plateformes dans leur travail de synthèse des contributions.

La majorité de cette contribution a été publiée dans l'article suivant : William Aboucaya, Rafael Angarita, and Valérie Issarny. "Detecting Obstacles to Collaboration in an Online Participatory Democracy Platform: A Use-case Driven Analysis". In: 44th International Conference on Software Engineering (ICSE'22). Proceedings of the 2nd International Workshop on Equitable Data and Technology. FairWare '22. Pittsburgh, Pennsylvania: Association for Computing Machinery, May 2022, pp. 25–33 [1]. La partie de ce chapitre liée aux graphes de connaissances a été publiée séparément, dans l'article suivant : William Aboucaya, Sonia Guehis, and Rafael Angarita. "Building Online Public Consultation Knowledge Graphs". In: Text2KG 2023: International Workshop on Knowledge Graph Generation from Text, Co-located with the ESWC 2023. Ed. by CEUR-WS. Hersonissos (Crete), Greece, May 2023, p. 12. url: https://inria.hal.science/hal-04076743. [2].

<footnote n="1">¹Nous avons conscience que cette question a déjà été traitée par d'autres chercheurs avant nous, notre objectif est d'identifier de nouveaux problèmes.</footnote>
**Objectifs et moyens des plateformes participatives contemporaines** Notre deuxième contribution est un cadre de description des plateformes participatives contemporaines basé sur deux caractéristiques différentes : les objectifs que les organisateurs d'initiatives participatives en ligne cherchent à atteindre et les moyens d'interactions entre les citoyens implémentés. Cette méthode a été proposée à partir d'une série d'entretiens semi-dirigés avec différentes parties prenantes de plateformes participatives. Ces entretiens nous permettent de comprendre les motifs qui poussent des institutions à créer et maintenir des plateformes participatives.

Ensuite, à partir des deux types de différentes proposés dans ce chapitre, nous proposons des moyens de réduire les problèmes identifiés en nous servant d'outils plus contemporains que ceux implémentés dans les plateformes actuelles. En l'occurrence, nous proposons d'implémenter de l'édition collaborative des contributions et d'utiliser des méthodes issues du Traitement Automatique du Langage (TAL).

Cette contribution a été produite en collaboration avec Rafael Angarita et Valérie Issarny.

**Améliorer la participation à grande échelle grâce au TAL** La troisième contribution de cette thèse est une méthodologie pour identifier des paires de propositions contradictoires et en accord dans une consultation en utilisant de l'Inférence sur le Langage Naturel (NLI dans le reste de ce résumé). Cette contribution a pour objectifs de faciliter le travail de synthèse des propositions par les administrateurs de plateformes ainsi que l'identification de propositions pertinentes pour un citoyen donné dans le contexte d'une plateforme participative à grande échelle. Ces objectifs sont issus de problèmes précédemment identifiés dans le contexte de la participation citoyenne à la fois en ligne et en personne. Notre méthodologie nous permet de construire des jeux de données permettant d'entraîner des modèles NLI à partir de textes produits directement sur des plateformes participatives, ainsi que d'utiliser ces modèles sur des paires de propositions citoyennes qui sont typiquement plus longues que les phrases communément utilisées pour faire du NLI.

Nous évaluons la pertinence de notre méthodologie pour améliorer la participation citoyenne à grande échelle à partir de deux cas d'usage. Le premier est l'identification de *clusters* de propositions en accord entre elles dans une consultation afin d'automatiser partiellement la synthèse des propositions par les administrateurs de plateformes. Le second cas d'usage est la recommandation, faite aux lecteurs d'une proposition donnée, d'autres propositions contenant des éléments de contradiction afin d'aider les citoyens à identifier les différents points de vue exprimés sur une question donnée.

Cette contribution a été produite en collaboration avec Oana Balalau, Rafael Angarita et Valérie Issarny.

# 2 Notions préliminaires

## 2.1 Participation citoyenne

Cette thèse étant orientée vers la participation citoyenne en ligne, il est important de commencer par présenter l'état de l'art sur ce sujet. Nous allons d'abord présenter
<figure><img src="image_3.png" /><figcaption>Figure 1: Schéma du workflow proposé par Parra et al. [128] pour décrire les budgets participatifs.</figcaption></figure>

la participation citoyenne en elle-même avant de mettre en avant quelques travaux d'autres chercheurs sur la participation en ligne et sur lesquels cette thèse se base.

La participation citoyenne est un ensemble de pratiques visant à donner aux citoyens un certain degré d'influence sur la gouvernance d'une institution spécifique à laquelle ils sont liés (par exemple, leur ville, leur université, le parlement de leur pays). Dans cette thèse, nous utiliserons la définition de la participation citoyenne donnée par Baum [22], selon laquelle la participation citoyenne est "l'implication des citoyens dans la prise de décision publique". Plusieurs avantages de la participation citoyenne ont été identifiés et sont régulièrement utilisés comme arguments en sa faveur. Un exemple d'avantage est la capacité des citoyens à produire des connaissances originales basées sur leur expertise ou leur expérience. L'inclusion de ces connaissances est considérée comme capitale dans les sociétés contemporaines où les citoyens sont capables de remettre en question ou de compléter l'expertise fournie par les institutions [23, 110]. Différents moyens ont été proposés pour caractériser la participation citoyenne. On peut par exemple citer l'échelle de la participation citoyenne d'Arnstein [15]. Cette échelle est composée de huit degrés de pouvoir citoyen croissant, de la manipulation jusqu'au contrôle citoyen. Chaque degré implique différentes formes d'interaction entre les citoyens et l'institution impliquée, mais aussi entre les citoyens eux-mêmes. Par ailleurs, Parra et al. [128] proposent différents espaces de conception pour les éléments informatisés d'un budget participatif à partir d'un * workflow * qu'ils définissent. Ce * workflow * peut être utilisé pour décrire la plupart des initiatives de participation citoyenne. En effet, l'identification des étapes du * workflow * au cours desquelles la contribution des citoyens est demandée dans un processus participatif donné peut être un moyen utile de le décrire. Pour mieux comprendre les différentes étapes potentielles d'un processus participatif, nous reproduisons le schéma représentant ce * workflow * dans la Figure 1.

La participation citoyenne en ligne a fait l'objet de nombreuses études en informatique, en particulier dans les domaines de recherche en TCAO et en IHM, comme l'indique Nelimarkka [117]. Ces études portent sur différents types de contextes participatifs, notamment les systèmes de pétition en ligne [74, 162, 178] ou les budgets participatifs [128, 182]. Les études mentionnées ci-avant sur les pétitions en ligne identifient des corrélations entre le nombre de pétitions sur un sujet donné et le nombre de signatures qu'elles reçoivent, analysent le comportement des utilisateurs les plus actifs à court et à long terme afin d'identifier leurs différences avec les autres signataires de pétitions, et identifient les indicateurs de succès d'une pétition tels qu'ils sont perçus par les contributeurs. Concernant le budget participatif, les au-
teurs identifient les facteurs essentiels incitant les citoyens à rester impliqués dans des campagnes récurrentes après la première année, et analysent les pratiques et technologies utilisées dans les différentes étapes des budgets participatifs en ligne contemporain afin d'identifier leurs failles. De plus, grâce à une revue de la littérature étudiant empiriquement la participation citoyenne, Ianniello et al. [75] identifient plusieurs obstacles principaux à la participation et proposent des solutions pour les surmonter. Les trois familles d'obstacles identifiées sont les facteurs contextuels (par exemple, l'attitude des parties prenantes), les dispositions de l'institution (par exemple, le design du processus participatif) et la gestion des processus (par exemple, la qualité de la collaboration citoyenne). Certaines conclusions de cette thèse visent à améliorer les moyens offerts par les plateformes participatives pour faciliter la collaboration citoyenne, réduisant ainsi le troisième obstacle identifié.

## 2.2 Traitement du langage

La recherche en TAL a proposé différentes méthodes qui peuvent être mises en œuvre pour résoudre certains des problèmes de la participation en ligne à grande échelle. Nous définissons ici quelques notions clés liées à l'utilisation du TAL dans cette thèse. Nous présentons également des recherches antérieures en TAL pour la participation citoyenne.

**Inférence sur le Langage Naturel :** L'Inférence sur le Langage Naturel (NLI) est une tâche qui consiste à prédire, à partir de deux textes, une prémisse et une hypothèse, si : i) l'hypothèse peut être déduite de la prémisse (implication) ; ii) la négation de l'hypothèse peut être déduite de la prémisse (contradiction) ; iii) ou si aucune des deux situations précédentes n'est vraie (neutre). Plusieurs jeux de données annotés ont été proposés pour cette tâche, tels que les corpus Stanford Natural Language Inference (SNLI) [30] et Cross-Lingual NLI (XNLI) [45]. Les systèmes les plus performants sur les benchmarks sont des modèles basés sur une architecture "Transformeur", tels que BERT [52] ou RoBERTa [97]. La recherche a montré que le NLI reste un défi complexe [67, 106].

**Étude du contenu textuel de plateformes participatives :** Pour répondre aux problèmes de surcharge d'informations touchant à la fois les citoyens et les administrateurs de plateformes participatives, plusieurs méthodes basées sur du TAL ont été proposées. Dans [13, 19, 14], les auteurs proposent grâce à différentes méthodes (modélisation thématique, similarité sémantique, etc.) de résumer ou regrouper automatiquement les propositions et les commentaires publiés par les utilisateurs. Dans [149, 150], les auteurs proposent d'extraire des arguments à partir des contributions des citoyens. Ils créent une taxonomie d'arguments et proposent une approche pour identifier les arguments à l'aide de règles syntaxiques. Le module d'extraction d'arguments est ensuite intégré dans un système de recherche par mots-clés.

## 2.3 Graphes de connaissances

Les graphes de connaissances représentent des faits sous forme de nœuds et de relations [70, 96]. Les deux principaux modèles de graphes sont RDF [103] et les graphes de propriétés [11]. Dans le modèle RDF, les nœuds sont des ressources dotées
d'un identifiant unique sur le web. Les arêtes relient les nœuds en construisant des triplets sujet-prédicat-objet. Les graphes de propriétés, eux, sont des multigraphes étiquetés et dirigés dans lesquels les nœuds et les arêtes peuvent avoir des propriétés sous la forme de paires clé-valeur. Les nœuds y représentent des entités et les arêtes, des relations entre ces entités.

La création d'un graphe de connaissances à partir d'un texte implique l'utilisation de techniques de TAL, notamment la reconnaissance d'entités nommées (NER) [113] et la liaison d'entités nommées (NEL) [154]. Le NER se concentre sur l'identification et la catégorisation des entités dans un texte, telles que les personnes, les organisations et les lieux, entre autres. Le NEL, quant à lui, relie les entités reconnues aux entités d'une base de connaissances. Plusieurs méthodes de construction de bases de connaissances spécifiques à un domaine ont été développées dans divers domaines, dont la mode, la médecine, etc. [21, 141, 140, 161].

## 3 Étude d'une plateforme participative existante

Les plateformes participatives répondent à divers cas d'usage, par exemple le budget participatif [71, 128], la publication de données ouvertes à des fins civiques [28, 94], la collecte et le traitement des opinions de grands groupes de citoyens [134], ainsi que la consultation et la délibération citoyenne, pour n'en citer que quelques-uns. Dans ce chapitre, nous nous concentrons sur un exemple du quatrième cas : la consultation en ligne "République Numérique"<fnref n="2" /> (abrégé en *RepNum* dans la suite de ce texte). Les consultations en ligne visent à permettre aux citoyens d'exprimer directement leurs opinions sur un sujet particulier – dans notre cas, les différents articles d'un projet de loi – en dehors de tout contexte électoral. Elles sont souvent considérées comme un moyen d'améliorer la participation des citoyens dans un système politique représentatif, bien qu'elles ne soient pas contraignantes pour l'institution organisatrice. Techniquement, les platesformes de consultation en ligne peuvent être considérées comme des plateformes de TCAO permettant aux utilisateurs de modifier de façon collaborative un ensemble de propositions indépendantes en leur ajoutant de nouveaux éléments ou en discutant des éléments ajoutés précédemment. Elles mettent généralement en œuvre au moins les trois formes de contributions suivantes :

1. L'Utilisateur A publie une proposition, qu'elle soit détaillée ou vague, sur la plateforme.

2. L'Utilisateur B commente la proposition précédemment publiée par l'Utilisateur A.

3. L'Utilisateur B vote pour, contre, ou exprime sa neutralité sur la proposition de l'Utilisateur A.

Dans cette partie de la thèse, nous visons à formuler des recommandations pour atténuer les biais dans le *design* de la plateforme afin d'améliorer l'engagement des citoyens dans les plateformes participatives en ligne. Notre objectif est d'aider à construire des plateformes participatives plus équitables sur la base des données produites dans le cadre d'expériences antérieures. Pour ce faire, nous analysons les

<footnote n="2">²https://www.republique-numerique.fr/</footnote>
contributions et les interactions des utilisateurs sur la plateforme *RepNum*. Cela nous amène à étudier les questions de recherche suivantes, à partir desquelles nous formulons des recommandations pour la conception de plateformes participatives :

**QR1:** Comment l'attention est-elle distribuée entre les propositions et peut-on identifier des facteurs spécifiques à la plateforme à l'origine de cette distribution ?

**QR2:** Comment les contributeurs les plus actifs utilisent-ils les différents types de contributions (proposition, modification de proposition, argument, source, vote)?

**QR3:** Quel est l'impact de la présentation des propositions sur la participation ?

Nous répondons à ces trois questions de recherche de la manière suivante. Tout d'abord, en représentant la consultation sous la forme d'un graphe, nous nous interrogeons sur l'existence de propositions et d'utilisateurs centralisant la conversation à l'aide de l'algorithme PageRank [125] et d'autres métriques. Cette analyse nous permet de comprendre si les contributions ont tendance à être centralisées autour de contributeurs "influents" ou autour de propositions considérées comme intéressantes. Nos résultats nous permettent de proposer des recommandations concernant le *design* de la consultation et l'affichage des propositions dans la plateforme. Ensuite, nous étudions les contributions des contributeurs les plus actifs pour comprendre s'ils ont tendance à se spécialiser dans un type de contribution spécifique (par exemple, écrire des propositions, voter sur les contributions des autres, etc.). Nous constatons que les votants les plus actifs ont tendance à être complètement séparés des autres contributeurs actifs qui produisent des contributions textuelles. En outre, nous montrons qu'il existe des contributeurs actifs à la fois spécialisés et polyvalents pour les contributions textuelles. Enfin, nous proposons une analyse statistique des réponses aux propositions en fonction de deux facteurs influençant leur positionnement dans l'interface utilisateur. Ces deux facteurs sont la catégorisation des propositions en fonction de la structure du projet de loi et l'épinglage manuel par les administrateurs de la plateforme de certaines propositions au sommet de chaque catégorie. Nous identifions pour ces deux facteurs des distributions inégales des réponses aux propositions, ce qui nous amène à considérer qu'ils induisent un biais dans la participation. Nous proposons donc des modifications de l'interface utilisateur visant à réduire ces biais.

De plus, afin d'atténuer les biais identifiés dans la conception de cette plateforme participative contemporaine, nous proposons d'améliorer la gamme d'outils mis à la disposition des citoyens et des administrateurs de plateformes pour trouver des propositions spécifiques. Pour ce faire, nous proposons de construire un graphe de connaissances des consultations publiques. Les propositions y sont associées à des sujets identifiés dans leur consultation ainsi qu'à des ressources externes issues du graphe de connaissances DBpedia [18]. Le graphe proposé est générique et peut être utilisé pour la plupart des consultations citoyennes en ligne. Ses contributions sont les suivantes : i) l'enrichissement des données d'une consultation publique en ligne avec des ressources liées pour améliorer la contextualisation des textes, faciliter son intégration avec d'autres sources de données liées, y compris d'autres consultations, accroître l'inclusivité, la transparence et la responsabilité, et améliorer la prise de décision ; ii) l'analyse des propositions pour déduire leurs sujets afin de mieux organiser, caractériser et comprendre les informations contenues dans les
consultations. En catégorisant les contributions d'une consultation en fonction de leurs sujets, il peut être plus facile d'identifier des tendances et des motifs dans l'opinion publique et de comparer les différents points de vue.

## 4 Objectifs et moyens des plateformes participatives contemporaines

L'utilisation de plateformes participatives en ligne est devenue plus courante ces dernières années, permettant l'organisation de processus participatifs à plus grande échelle [12, 91], à la fois en ce qui concerne leur nombre de contributeurs et la distance géographique qui sépare ces derniers. Ces plateformes implémentent un large éventail de fonctionnalités pour aider les citoyens à contribuer à un projet participatif. Ces fonctionnalités mettent en œuvre les règles d'un processus participatif, telles que définies par l'institution organisatrice. Elles définissent également le degré d'interaction possible entre les contributeurs d'une plateforme donnée, permettant, par exemple, la création de versions modifiées d'une contribution préexistante. Cependant, Ianniello et al. [75] identifient que la mauvaise qualité du design d'un processus participatif ou de la collaboration citoyenne a été un obstacle majeur à la réussite de processus participatifs par le passé. Le Chapitre 3 de cette thèse identifie également des défauts dans le design d'une consultation en ligne spécifique et de son interface utilisateur, ce qui a entraîné des biais mesurables dans les contributions produites par les citoyens. Par conséquent, nous considérons que les fonctionnalités mises en place sont un facteur clé de la réussite des initiatives participatives en ligne.

Outre les fonctionnalités qu'elles implémentent, les plateformes participatives se distinguent également en fonction de leurs objectifs. L'un des moyens de caractériser les objectifs d'un processus participatif peut être d'utiliser le type de résultat attendu par l'institution. Cette différenciation recoupe en partie celle proposée par l'échelle de participation citoyenne d'Arnstein [15] puisque les degrés les plus hauts de l'échelle – appelés “degrés de pouvoir citoyen” – ne peuvent être atteints si la contribution attendue est simple et individuelle (par exemple, répondre à un sondage). De plus, bien que les administrateurs de plateformes ne soient pas nécessairement conscients des spécificités de chaque degré de participation, ils identifient clairement le type de contributions attendu sur les plateformes qu'ils gèrent et peuvent donc être plus à même de décrire les contributions produites par les contributeurs que le positionnement de leur plateforme sur l'échelle d'Arnstein. Par conséquent, nous considérons que le type de contributions attendu est un critère plus approprié pour différencier les objectifs des plateformes participatives que leur degré de participation citoyenne.

à partir des deux axes de différenciation des plateformes participatives mentionnés ci-dessus – objectifs poursuivis et fonctionnalités implémentées –, ce chapitre répond aux questions de recherche suivantes :

**QR1:** Quels sont les différents objectifs poursuivis par les institutions responsables de plateformes participatives ?

**QR2:** Comment les fonctionnalités mises en œuvre dans les plateformes participatives impactent-elles le design des processus participatifs ?
Pour répondre à ces questions, nous nous basons sur une série d'entretiens semi-dirigés avec des administrateurs et des concepteurs de plateformes participatives. Les questions que nous avons posé aux participants portaient sur les objectifs que cherchaient à remplir leurs plateformes, les fonctionnalités implémentées et les moyens employés pour récolter et produire une synthèse des contributions. Nous nous basons également sur une étude du fonctionnement de différentes consultations qui se sont basée sur les mêmes plateformes logicielles et qui permettaient la production de contributions textuelles, ainsi que sur les contributions qui y ont été produites. Pour assurer la représentativité de notre panel d'interviewés, nous avons choisi d'interroger des acteurs aux profils professionnels variés, issus de villes de tailles différentes et qui utilisent des logiciels différents pour leurs plateformes participatives. Ces entretiens nous permettent d'identifier quatre objectifs différents et non-exclusifs que les institutions organisatrices de processus participatifs peuvent chercher à remplir : sonder les opinions des citoyens, mettre en place des espaces de dialogue entre les citoyens et une institution, créer des communautés actives de citoyens et leur donner les moyens de porter leur voix dans l'espace public, et co-construire des projets. Nous présentons les caractéristiques de chacun de ces objectifs avant de souligner les éventuels problèmes que rencontrent les institutions à l'origine des plateformes associées. Nous proposons également un second axe de différentiation basé sur les moyens d'interaction implémentés dans les différentes plateformes. Nous distinguons deux types plateformes. Les premières sont les "boîtes à idées", qui implémentent presque exclusivement des moyens d'interaction entre les citoyens et une institution. Les secondes sont les plateformes "interactives", dans lesquelles de multiples moyens d'interaction entre les citoyens sont implémentés. Nous présentons les possibilités offertes par chacun de ces deux types de plateformes ainsi que les défauts auxquels leurs implémentations contemporaines sont confrontées. À partir des différents objectifs et caractéristiques identifiés lors de nos entretiens, nous proposons des recommandations pour améliorer l'expérience des contributeurs et des administrateurs des plateformes de participation citoyenne, afin de guider les administrateurs et les concepteurs de plateformes dans le développement de futures plateformes participatives. Ces recommandations sont axées sur l'identification des contributions pertinentes grâce à l'apprentissage automatique, l'édition collaborative des contributions, et des fonctionnalités préexistantes qui n'ont pas été mises en œuvre dans le contexte de plateformes participatives.

## 5 Améliorer la participation à grande échelle grâce au TAL

Dans certains cas, les plateformes participatives permettent aux citoyens de voir et d'interagir avec les contributions d'autres personnes, généralement selon un modèle de collaboration asynchrone. Cette partie de la thèse s'intéresse spécifiquement à ces derniers cas où des interactions de nature collaborative entre les citoyens sont rendues possibles. Cependant, bien que les plateformes participatives permettent de co-construire des projets publics à grande échelle, plusieurs études rapportent que les citoyens et les décisionnaires rencontrent des problèmes à plusieurs stades du processus participatif. Dans [75], les auteurs identifient plusieurs risques, dont le manque de communication entre l'institution et les citoyens, le manque de transparence des
critères de sélection de propositions, ou la difficulté à atteindre des accords, voire des consensus. Une autre difficulté majeure pour ce type d'initiatives est la sur-charge d'information qui peut affecter à la fois les citoyens et les administrateurs de plateformes [38, 132].

Ce travail présente un outil permettant de détecter les équivalences et les contra-
dictions dans les propositions des utilisateurs. Notre outil permet aux utilisateurs
de trouver des propositions intéressantes - qu'elles soient équivalentes ou contradic-
toires - et de se rapprocher de leurs auteurs, tandis que les institutions peuvent s'en
servir pour créer des projets composés d'ensembles de propositions sémantiquement
cohérentes. La cohérence sémantique, par opposition à la cohérence syntaxique mise
en œuvre par les algorithmes de gestion de cohérence pour l'édition collaborative (par
exemple, [8]), est définie comme l'absence de contradiction entre différents textes.
Les contradictions qui nous intéressent peuvent être de différentes natures, allant de
la contradiction idéologique à des désaccords numériques [50]. Nous nous concen-
trons sur des projets composés de propositions émanant de différents utilisateurs.
Nous considérons alors qu'un projet est sémantiquement cohérent s'il ne contient au-
cune paire de propositions contradictoires. Garantir automatiquement la cohérence
sémantique aidera les citoyens à construire des projets communs complexes et bien
développés en mettant en évidence les contradictions potentielles dans leurs parties
constitutives. En outre, la détection de paires de propositions en accord ou con-
tradictoires aidera les responsables de la plateforme à identifier des communautés
d'idées et à résumer leurs revendications. Nous présentons une approche basée sur le
TAL pour identifier les paires de propositions contradictoires par le biais de la clas-
sification de texte. Plus précisément, notre objectif peut être identifié comme une
tâche de NLI, également appelée dans la littérature "implication textuelle". Parikh
et al. [126] définissent la NLI comme "le problème qui consiste à établir des relations
d'implication et de contradiction entre une prémisse et une hypothèse". Dans notre
cas, nous essayons de prédire si deux propositions sont en accord, contradictoires ou
neutres. Nous proposons une méthodologie pour produire des modèles permettant
de détecter les propositions contradictoires. Nous appliquons notre méthode à deux
consultations citoyennes francophones, *RepNum* et "*Revenu Universel d'Activité*"<fnref n="3" />
(RUA). Nous avons choisi ces consultations parce qu'elles ont reçu un grand nombre
de propositions (respectivement 692 et 1760) et qu'elles ont permis aux contributeurs
de soumettre des commentaires pour ou contre une proposition donnée, que nous
exploitons pour entraîner les modèles. Par ailleurs, les contributions soumises dans
le cadre de ces consultations sont généralement plus longues que les paires de phrases
trouvées dans la plupart des corpus de NLI. Par conséquent, nous considérons notre
travail comme une étape importante vers l'application du NLI aux textes de grande
taille. Pour valider notre approche, nous proposons deux évaluations basées sur des
cas d'usage concrets. Tout d'abord, nous évaluons la capacité de nos modèles à
identifier des communautés de propositions sémantiquement cohérentes entre elles.
Cette utilisation permet d'améliorer la capacité des administrateurs de plateformes
à produire des synthèses de consultations à grande échelle grâce à un pré-traitement.
Ensuite, réalisons une étude utilisateur comparant la pertinence de nos modèles NLI
à la similarité textuelle sémantique, une technique qui a déjà été proposée pour or-
ganiser les contributions dans des consultations citoyennes [13, 19], pour une tâche
de recommandation de propositions à des citoyens.

<footnote n="3">³https://www.consultation-rua.gouv.fr/</footnote>
# 6 Conclusions générales

Le gain en popularité de la participation citoyenne en ligne durant les dernières années a rendu capitale la résolution des problèmes affectant les plateformes participatives afin d'inclure davantage les citoyens dans les prises de décisions publiques. En nous basant sur les problèmes que nous avons identifié et sur des travaux pré-existants, nous proposons diverses solutions pour améliorer la participation en ligne. Ces solutions sont ont pour objectifs de permettre une plus grande collaboration entre les citoyens et de faciliter la participation à grande échelle.

Les contributions de cette thèse sont les suivantes : i) Nous identifions des biais dans le design de la plateforme participative associée à la consultation République Numérique à partir des interactions entre les citoyens et les contributions. Ces différents biais identifiés nous permettent de proposer cinq recommandations visant à améliorer les plateformes participatives. ii) Nous proposons une méthodologie afin de construire un graphe de connaissances pour la participation citoyenne. Nous utilisons ce graphe de connaissances pour permettre une meilleure intéropérabilité entre les différentes consultations et pour enrichir les contributions publiées par les citoyens grâce à des ressources externes. iii) Nous proposons un cadre de description des plateformes de participation citoyenne basé sur les objectifs que les organisateurs cherchent à atteindre et sur les moyens d'interaction entre citoyens implémentés. Ce cadre de description est basé sur une série d'entretiens semi-dirigés avec différentes parties prenantes de la participation citoyenne en France et en Espagne. iv) Nous proposons de faciliter la participation en ligne à grande échelle en identifiant des paires de propositions en accord et en contradiction dans des plateformes participatives. Nous proposons et implémentons une méthodologie visant à produire des classificateurs capables d'effectuer cette tâche sur des contributions citoyennes. Les classificateurs que nous produisons sont utilisés pour évaluer la pertinence de notre méthodologie pour deux cas d'usage différents.

## 6.1 Perspectives

Les travaux effectués dans cette thèse nous permettent de proposer de nouvelles directions de recherche. Tout d'abord, le graphe de connaissances que nous avons proposé permet déjà une amélioration de la recherche d'informations et de la contextualisation des propositions d'une consultation citoyenne. Cependant, nous identifions les pistes d'amélioration suivantes : une intégration de sources de données externes non représentées sous la forme de graphes de connaissances (par exemple, l'Assemblée Nationale française), et une évaluation empirique par des testeurs indépendants de la qualité de nos annotations reliant les propositions à des ressources DBpedia.

De plus, bien que nous ayons proposé une étude des objectifs des organisateurs de processus participatifs en ligne et des moyens d'interactions qu'ils mettent en place, nos travaux n'incluent pas de citoyens impliqués dans des plateformes participatives. Quelques travaux ont déjà été proposés sur les motivations des citoyens dans certaines plateformes participatives spécifiques [74, 116, 146], mais il n'en existe aucune à notre connaissance s'intéressant à plusieurs plateformes différentes. Par conséquent, nous considérons qu'il serait important que de futurs travaux soient dédiés à l'étude des pratiques et des motivations des citoyens impliqués dans des
processus participatifs en ligne. Pour ce faire, nous considérons que la méthode des entretiens semi-dirigés est la plus adaptée.

Par ailleurs, la participation citoyenne prend des formes diverses et les plate-
formes participatives ne sont pas les seuls endroits où des institutions recueillent
l'opinion de citoyens sur des sujets particuliers. L'exemple le plus fréquent est
probablement les réseaux, bien que ces derniers ne soient presque jamais mis en
interaction avec les plateformes participatives. Par conséquent, de futurs travaux
pourraient être proposés à la fois concernant l'analyse comparative de différents
contextes de participation en ligne, et proposant des moyens d'interaction entre ces
différentes plateformes.

Enfin, les contributions présentées dans cette thèse sont focalisées sur des aspects
spécifiques de la participation citoyenne en ligne, ainsi que sur certains problèmes
que rencontrent ses parties prenantes. Cependant, nous n'avons pas encore pro-
posé d'intégration de toutes ces contributions dans une même plateforme participa-
tive. Il est donc important de formaliser un nouveau * workflow * pour la participation
citoyenne à grande échelle à partir des contributions produites afin de faciliter la col-
laboration citoyenne et de réduire les problèmes que nous et d'autres chercheurs
ont identifié. Une implémentation sur une plateforme participative * open source * telle
que Decidim [51] ou Consul [47] permettrait de fournir un exemple de ce * workflow *.
# Contents

<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>1</td><td>Introduction</td><td>27</td></tr><tr><td>1.1</td><td>Scope of the thesis</td><td>28</td></tr><tr><td>1.2</td><td>Contributions</td><td>29</td></tr><tr><td>1.3</td><td>Outline of the thesis</td><td>31</td></tr><tr><td>2</td><td>Preliminaries</td><td>33</td></tr><tr><td>2.1</td><td>Definitions</td><td>33</td></tr><tr><td>2.2</td><td>Online citizen participation</td><td>35</td></tr><tr><td>2.2.1</td><td>Citizen participation</td><td>35</td></tr><tr><td>2.2.2</td><td>Analyses of existing online citizen participation contexts</td><td>37</td></tr><tr><td>2.2.3</td><td>Improving online participatory platforms</td><td>39</td></tr><tr><td>2.3</td><td>Large-scale online collaboration</td><td>40</td></tr><tr><td>2.3.1</td><td>Asynchronous editing systems</td><td>41</td></tr><tr><td>2.3.2</td><td>Synchronous editing systems</td><td>42</td></tr><tr><td>2.4</td><td>Knowledge graphs</td><td>44</td></tr><tr><td>2.5</td><td>NLP and machine learning</td><td>45</td></tr><tr><td>2.6</td><td>Conclusion</td><td>47</td></tr><tr><td>3</td><td>Analysis of an existing participatory platform</td><td>49</td></tr><tr><td>3.1</td><td>Introduction</td><td>49</td></tr><tr><td>3.2</td><td>The République Numérique citizen consultation</td><td>51</td></tr><tr><td>3.3</td><td>Does the platform influence how users contribute?</td><td>53</td></tr><tr><td>3.3.1</td><td>Analyzing the distribution of contributions across users</td><td>53</td></tr><tr><td>3.3.2</td><td>Analyzing the factors influencing the contributions</td><td>54</td></tr><tr><td>3.4</td><td>How to mitigate the influence of the most active users?</td><td>57</td></tr><tr><td>3.5</td><td>Does the positioning of Proposals have an impact on participation?</td><td>59</td></tr><tr><td>3.6</td><td>Building an Online Public Consultation Knowledge Graph</td><td>60</td></tr><tr><td>3.6.1</td><td>Topic modeling</td><td>60</td></tr><tr><td>3.6.2</td><td>Entity Recognition and Linking</td><td>61</td></tr><tr><td>3.6.3</td><td>Resulting Knowledge Graph</td><td>63</td></tr><tr><td>3.7</td><td>Threats to validity</td><td>63</td></tr><tr><td>3.8</td><td>Related Work</td><td>64</td></tr><tr><td>3.9</td><td>Conclusion</td><td>66</td></tr><tr><td>4</td><td>Objectives and features of contemporary platforms</td><td>69</td></tr><tr><td>4.1</td><td>Introduction</td><td>69</td></tr><tr><td>4.2</td><td>Methodology</td><td>71</td></tr><tr><td>4.3</td><td>Different objectives of citizen participation platforms</td><td>73</td></tr><tr><td>4.3.1</td><td>Surveying citizens' opinions</td><td>73</td></tr></tbody></table>
<table><tbody><tr><td>4.3.2</td><td>Dialogue between citizens and an institution</td><td>74</td></tr><tr><td>4.3.3</td><td>Creation of opinion groups or neighborhood councils</td><td>75</td></tr><tr><td>4.3.4</td><td>Co-construction of projects</td><td>76</td></tr><tr><td>4.4</td><td>Different types of platforms for contemporary online citizen participation</td><td>77</td></tr><tr><td>4.4.1</td><td>“Suggestion box”-like platforms</td><td>77</td></tr><tr><td>4.4.2</td><td>Interactive platforms</td><td>79</td></tr><tr><td>4.5</td><td>How to enrich online participatory platforms</td><td>80</td></tr><tr><td>4.5.1</td><td>Collaborative systems</td><td>80</td></tr><tr><td>4.5.2</td><td>Natural Language Processing for large-scale processing of contributions</td><td>81</td></tr><tr><td>4.6</td><td>Threats to validity</td><td>83</td></tr><tr><td>4.7</td><td>Conclusion</td><td>83</td></tr><tr><td><b>5</b></td><td><b>Improving large scale participation using NLI</b></td><td><b>85</b></td></tr><tr><td>5.1</td><td>Introduction</td><td>85</td></tr><tr><td>5.2</td><td>Methodology</td><td>87</td></tr><tr><td>5.2.1</td><td>Problem statement</td><td>87</td></tr><tr><td>5.2.2</td><td>Weakly-labeled data for model training</td><td>89</td></tr><tr><td>5.2.3</td><td>Creating a gold dataset for evaluation</td><td>89</td></tr><tr><td>5.2.4</td><td>Automatically labeling long texts</td><td>90</td></tr><tr><td>5.3</td><td>Datasets</td><td>91</td></tr><tr><td>5.4</td><td>Evaluation</td><td>95</td></tr><tr><td>5.4.1</td><td>Fine-tuned models</td><td>95</td></tr><tr><td>5.4.2</td><td>Insights and error analysis</td><td>98</td></tr><tr><td>5.5</td><td>Assessing the integration of an NLI model into a citizen participation platform</td><td>100</td></tr><tr><td>5.5.1</td><td>Identifying idea communities in a consultation</td><td>101</td></tr><tr><td>5.5.2</td><td>Recommending contributions to citizens during their navigation</td><td>103</td></tr><tr><td>5.6</td><td>Interview with stakeholders</td><td>105</td></tr><tr><td>5.7</td><td>Conclusion</td><td>107</td></tr><tr><td><b>6</b></td><td><b>General conclusions</b></td><td><b>109</b></td></tr><tr><td>6.1</td><td>Contributions</td><td>109</td></tr><tr><td>6.1.1</td><td>Analysis of a participatory platform’s design</td><td>109</td></tr><tr><td>6.1.2</td><td>Building a knowledge graph for citizen consultations</td><td>110</td></tr><tr><td>6.1.3</td><td>Identification of the different objectives and features implemented in participatory platforms</td><td>110</td></tr><tr><td>6.1.4</td><td>NLI for large-scale participation</td><td>110</td></tr><tr><td>6.2</td><td>Future research directions</td><td>111</td></tr><tr><td>6.2.1</td><td>Knowledge graphs</td><td>111</td></tr><tr><td>6.2.2</td><td>Qualitative analysis of interviews with stakeholders of citizen participation</td><td>111</td></tr><tr><td>6.2.3</td><td>Interaction of citizen participation platforms with other participation channels</td><td>111</td></tr><tr><td>6.2.4</td><td>Fine-grained identification of contradictions in proposals</td><td>112</td></tr><tr><td>6.2.5</td><td>Formalized workflow for online citizen participation</td><td>112</td></tr></tbody></table>
List of Figures

<table><tr><td>2.1</td><td>CSCW matrix</td><td>35</td></tr><tr><td>2.2</td><td>Workflow for participatory budgeting</td><td>36</td></tr><tr><td>3.1</td><td>Organization of categories in the RepNum consultation</td><td>52</td></tr><tr><td>3.2</td><td>PageRank of users in ascending order.</td><td>54</td></tr><tr><td>3.3</td><td>PageRanks correlation for different reply types.</td><td>54</td></tr><tr><td>3.4</td><td>Correlation of the amount of replies to a proposal for different contribution types.</td><td>56</td></tr><tr><td>3.5</td><td>Correlation of activity for each contribution type.</td><td>57</td></tr><tr><td>3.6</td><td>Steps for building the Online Public Consultation KG.</td><td>60</td></tr><tr><td>3.7</td><td>LDA representation of the topics discussed in a citizen consultation</td><td>61</td></tr><tr><td>3.8</td><td>Visualization of a generic consultation stored in JSON-LD.</td><td>64</td></tr><tr><td>4.1</td><td>Arnstein's ladder of citizen participation</td><td>70</td></tr><tr><td>5.1</td><td>A proposal with its arguments in the RepNum platform</td><td>92</td></tr><tr><td>5.2</td><td>Confusion matrix for the RUA dataset using the "XNLI → RUA" model</td><td>97</td></tr><tr><td>5.3</td><td>Confusion matrix for the RepNum dataset using the "RepNum + RUA" model.</td><td>97</td></tr><tr><td>5.4</td><td>Graph representation of a RepNum section</td><td>102</td></tr></table>
List of Tables

<table><tr><td>3.1</td><td>Possible types of replies for a contribution.</td><td>51</td></tr><tr><td>3.2</td><td>Number of contributions of all types by user profile.</td><td>52</td></tr><tr><td>3.3</td><td>NER and NEL results using DBpedia Spotlight on proposals of the RepNum consultation.</td><td>63</td></tr><tr><td>3.4</td><td>Amount of replies to contributions of all types for each category of the consultation.</td><td>67</td></tr><tr><td>3.5</td><td>Amount of replies to governmental contributions of all types for each category of the consultation.</td><td>67</td></tr><tr><td>4.1</td><td>Questions for the interviews</td><td>72</td></tr><tr><td>4.2</td><td>Profiles of interviewees</td><td>72</td></tr><tr><td>4.3</td><td>Categorization of platforms according to their objectives</td><td>73</td></tr><tr><td>4.4</td><td>Categorization of platforms according to their features</td><td>78</td></tr><tr><td>4.5</td><td>Summary of the issues identified and means proposed to solve them</td><td>82</td></tr><tr><td>5.1</td><td>Distribution of NLI labels for pairs of texts used for models training</td><td>91</td></tr><tr><td>5.2</td><td>Examples of pairs of proposals from the RUA consultation with their gold label</td><td>94</td></tr><tr><td>5.3</td><td>Distribution of NLI labels in the gold dataset</td><td>95</td></tr><tr><td>5.4</td><td>Results of the different models for the RUA gold dataset</td><td>96</td></tr><tr><td>5.5</td><td>Results of the different models for the RepNum gold dataset</td><td>98</td></tr><tr><td>5.6</td><td>Examples of pairs of proposals incorrectly labeled</td><td>99</td></tr><tr><td>5.7</td><td>Distribution of NLI labels for pairs of proposals in the RepNum and RUA consultations</td><td>101</td></tr><tr><td>5.8</td><td>Results of the clustering using the Louvain algorithm</td><td>103</td></tr><tr><td>5.9</td><td>Results of our experiment assessing the suitability of our model for recommendation.</td><td>104</td></tr></table>
# Chapter 1

# Introduction

## Contents

<table><tr><td>1.1 Scope of the thesis</td><td>28</td></tr><tr><td>1.2 Contributions</td><td>29</td></tr><tr><td>1.3 Outline of the thesis</td><td>31</td></tr></table>

During the recent decades, citizen participation has become increasingly popular to help citizens take part in political processes. This interest in citizen participation has been the strongest at the local scale (e.g., a city, a neighborhood) [100, 107, 155] but has also a few noteworthy examples at larger (e.g., national) scale [5, 56]. A first milestone concerning the implementation of citizen participation can be found in the participatory budgeting proposed in 1989 by the Porto Alegre municipality [155]. However, the origin of this idea is often traced back to the 1960's with activist groups such as civic rights and women liberation movements [130] in the USA or non-communist left-leaning groups in other countries [109]. Citizen participation regroups an ensemble of practices aiming at introducing some form of citizen input in policy making. These different practices imply multiple types of inputs asked from citizens, from individual responses to a survey to collective assessment of a project. Moreover, the degree of implication and of constraint power given to citizens greatly differ, from descending information of citizens by a governing body to the collective elaboration of a project by citizens [15].

Citizen participation has been a topic of interest for multiple academic research fields, especially in social sciences. Political sciences have proposed multiple analyses on the impact [155] and different interpretations [35] of citizen participation. Communication sciences have studied the discourse which has accompanied different citizen participation initiatives or promotion of citizen participation itself [5, 40]. Sociological studies have analyzed the degree of involvement of different social groups in citizen participation processes [59] and the different reasons motivating their involvement [116, 182]. Multiple other fields such as research in law, education or philosophy have also produced contributions to the study of citizen participation. Finally, the creation of online citizen participation platforms has led to the interest of computer science researchers in different fields. Computer science researchers have studied multiple aspects of online citizen participation, including participatory platforms' Human-Computer Interfaces (HCI) or text mining on citizen contributions.
## 1.1 Scope of the thesis

Online citizen participation, as well as other topics in the field of civic tech<fnref n="1" />, has become increasingly popular in recent years as a tool to better integrate citizens in public decision making. Online citizen participation can take multiple shapes, such as participatory budgeting [71, 128], gathering and releasing civic open data [28, 94] or citizen consultation [83, 148]. Moreover, participatory platforms have addressed a wide range of topics, including public space planning [176], digital transformation of a country [91] or climate change mitigation and adaptation [63, 133]. However, two constants can be identified and are considered here as constitutive of online citizen participation: an online platform where citizens can produce and publish their contributions, and an organizing body<fnref n="2" /> (e.g., municipal authority, university) which initiates the participatory process.

In this Ph.D. thesis, we aim at solving issues faced by both citizens and platforms administrators in contemporary participatory platforms, viewed as social computing systems. Some of these issues have been identified by previous works [38, 75, 132] and others are identified in Chapters 3 and 4 of this thesis. More specifically, we focus on reducing the information overload faced by citizens and platforms administrators and facilitating collaboration among citizens in the context of large-scale online participation. The main reason why we choose to focus mainly on citizen participation at large scale is because we consider the scalability to be one of the main advantage of online participation over its offline counterpart. For example, in 1998, Robert Dahl highlights the fact that letting each citizen express themself for ten minutes in a city of 10,000 citizens would require more than 200 days at a rate of 8 hours of discussion per day [48]. While this issue is particularly relevant for offline participation, it is largely reduced online thanks to the possibility given to citizens to express or read one another's expression at any time and the absence of the need for a physical space where these contributions to the debate can be expressed. Online platforms therefore allow citizen participation to take place at many different scales, from a neighborhood [146] to multiple countries [20].

To solve the aforementioned issues, we base our works on state-of-the-art contributions from the Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW) and Natural Language Processing (NLP) fields of computer science research. We propose to answer the two following questions:

1. What are the issues faced by citizens and administrators in online citizen participation platforms?<fnref n="3" />

2. Based on state-of-the-art computer science research, how can we reduce the issues identified to produce more collaborative and viable at large scale participatory platforms?

Finally, we have to acknowledge the fact that improvements in the technical functioning of participatory platform will not solve by itself the different crises faced by

<footnote n="1">¹Civic tech refers to the different technologies and related practices aiming at giving a more important role to citizens in democratic processes.</footnote>

<footnote n="2">²Often mentioned in this thesis as the “institution”</footnote>

<footnote n="3">³We acknowledge that this question has already been tackled by previous research works and aim at further investigating this topic.</footnote>
representative democracy [62, 99, 163]. Recent demands, for example by the *Gilets Jaunes* [66, 93] or 15-M/*Indignados* [57, 144] movements, for a higher degree of involvement of citizens in public decision making cannot be met only by more effective technical systems for participation but also require fundamental institutional changes to give actual weight to citizens’ proposals. In short, this thesis is not written with a techno-solutionist ambition to solve the different problems faced by citizen participation (e.g., lack of representation of minority groups [58], denial of political conflict [112], etc.), but with the objective of detecting existing issues undermining citizen participation in contemporary online platforms and proposing means to solve them.

## 1.2 Contributions

During this thesis, we produced contributions focused on three different approaches to improve collaboration in online large-scale citizen participation. The said contributions are each the topic of a chapter of this thesis and aim at identifying or solving issues faced by online citizen participation at large scale by leveraging different fields of computer science research.

**Analysis of a participatory platform** The first contribution of this thesis is an analysis of a specific citizen consultation focused on gathering opinions and new articles for the *République Numérique* law bill. We study the interactions between contributions and contributors of this citizen consultation to identify flaws in the design of the platform. We propose user interface and platform design modification recommendations to mitigate the impact of the flaws identified.

First, using a graph representation of the consultation, we question the existence of proposals and users centralizing the conversation using the PageRank algorithm [125] and other metrics. This analysis allows us to understand whether contributions tend to be centralized around “influential” contributors or around proposals viewed as interesting. Our results allow us to propose recommendations concerning the design of the consultation and the display of proposals in the platform.

Then, we study the contributions made by the most active contributors to understand whether they tend to be specialized in one specific type of contribution (e.g., writing proposals, voting on others’ contributions, etc.) or not. We identify a tendency for the most active voters to be completely separate from other active contributors who produce text-based contributions. Moreover, we also highlight the existence of both specialized and versatile active contributors for text-based contributions.

Finally, we propose a statistical analysis of the responses to proposals based on two factors influencing their positioning in the user interface. These two factors are the categorization of the proposals according to the structure of the law bill and the manual pinning by platform administrators of certain proposals at the top of each category. We identify for both factors uneven distributions of replies to proposals, which leads us to consider them as inducing bias in participation. We therefore propose user interface modifications to reduce the said biases.

Additionally, we propose a knowledge graph-based representation of online citizen consultations. This representation allows us to associate proposals with different topics and entities from the DBpedia knowledge base. We apply this representation
to the *République Numérique* online consultation to improve the possibilities to retrieve relevant proposals for a citizen and to summarize contributions for platforms administrators.

This contribution has been published in the following article: William Aboucaya, Rafael Angarita, and Valérie Issarny. "Detecting Obstacles to Collaboration in an Online Participatory Democracy Platform: A Use-case Driven Analysis". In: 44th International Conference on Software Engineering (ICSE'22). Proceedings of the 2nd International Workshop on Equitable Data and Technology. FairWare '22. Pittsburgh, Pennsylvania: Association for Computing Machinery, May 2022, pp. 25-33 [1]. The knowledge graph-related part of the chapter has been published in the following article: William Aboucaya, Sonia Guehis, and Rafael Angarita. "Building Online Public Consultation Knowledge Graphs". In: Text2KG 2023: International Workshop on Knowledge Graph Generation from Text, Co-located with the ESWC 2023. Ed. by CEUR-WS. Hersonissos (Crete), Greece, May 2023, p. 12. url: https://inria.hal.science/hal-04076743. [2].

**Objectives and features of contemporary participatory platforms** Our second contribution is a framework to describe contemporary online citizen participation platforms based on two different characteristics: the objectives pursued by platform maintainers and the features for interaction they implement. This method has been proposed based on a series of semi-structured interviews with participatory platform administrators, designers and technical advisors. These interviews allow us to understand the motivations pursued by institutions when they decide to create and maintain a participatory platform.

First, we propose a differentiation of participatory platforms based on the objectives pursued by institutions organizing participatory processes. The interviews we conducted allow us to identify four different and non-exclusive objectives: surveying citizens' opinions, organizing dialogue spaces between citizens and an institution, creating citizen groups and giving public recognition to their inputs, and co-construction of projects. For each of these objectives, we present their characteristics and highlight potential issues faced by maintainers of these platforms.

Then, we complete this first differentiation with another one focused on the functionalities for interaction implemented in the platforms. We distinguish two types of platforms. The first one is "suggestion box"-like platforms, which mainly implement possibilities for interaction between citizens and a given institution. The second one is interactive platforms in which multiple features for interaction between contributors are implemented. We present the possibilities given by both types of platforms and the flaw faced by their contemporary implementations.

Finally, based on the two different types of distinction proposed in this contribution, we propose means to reduce the issues identified using state-of-the-art technologies, namely collaborative editing of contributions and Natural Language Processing.

This contribution has been produced in collaboration with Rafael Angarita and Valérie Issarny.

**Natural Language Inference for large-scale citizen participation** The third contribution of this thesis is a methodology to identify contradictory and entailed pairs of proposals using Natural Language Inference (NLI). This contribution aims
at facilitating the summarization of proposals by platform administrators and the identification of relevant proposals for contributors in the context of large-scale on-line participatory platforms. These objectives originate from previously identified issues in online and offline citizen participation. Our methodology allows us to build datasets for the training of NLI models using content produced directly on citizen participation platforms and to use these models on pairs of proposals, which are typically longer than one-sentence inputs commonly used in NLI tasks.

After proposing a methodology for identification of entailed and contradictory proposals, we assess it using the contributions produced in two citizen consultations in French language: the *République Numérique* and *Revenu Universel d'Activité* consultations. Our work leverages state-of-the-art transformer architecture-based language models to perform Natural Language Inference on large texts in French.

Finally, we assess the suitability of our methodology to improve large-scale online participation based on two use cases. The first use case is the identification of clusters of entailed proposals in a consultation to partially automate the summarization of proposals by platforms administrators. This use case is assessed using graph-related metrics. The second use case is the recommendation, to the readers of a first proposal, of other proposals which contain elements of contradiction to help citizens identify the different views in a specific debate. This use case is assessed using an empirical evaluation of the quality of proposals recommended.

This contribution has been produced in collaboration with Oana Balalau, Rafael Angarita and Valérie Issarny.

## 1.3 Outline of the thesis

The remainder of this thesis is structured as follows. Chapter 2 presents the preliminary notions as well as previous work by other researchers used in the rest of this thesis. Chapter 3 is an analysis focused on the interaction of contributors with a specific citizen participation platform. In Chapter 4, we propose a method to describe contemporary online citizen participation platforms based on their objectives and the features they implement. In Chapter 5, we propose and assess a methodology to facilitate large-scale online citizen participation using Natural Language Inference. Finally, Chapter 6 summarizes the contributions of this thesis, proposes some final remarks and presents potential future research directions.
# Chapter 2

# Preliminaries

## Contents

<table><tr><td>2.1 Definitions</td><td>33</td></tr><tr><td>2.2 Online citizen participation</td><td>35</td></tr><tr><td>2.2.1 Citizen participation</td><td>35</td></tr><tr><td>2.2.2 Analyses of existing online citizen participation contexts</td><td>37</td></tr><tr><td>2.2.3 Improving online participatory platforms</td><td>39</td></tr><tr><td>2.3 Large-scale online collaboration</td><td>40</td></tr><tr><td>2.3.1 Asynchronous editing systems</td><td>41</td></tr><tr><td>2.3.2 Synchronous editing systems</td><td>42</td></tr><tr><td>2.4 Knowledge graphs</td><td>44</td></tr><tr><td>2.5 NLP and machine learning</td><td>45</td></tr><tr><td>2.6 Conclusion</td><td>47</td></tr></table>

## 2.1 Definitions

In this section, we will define the key concepts for the understanding of this thesis. This includes concepts from both citizen participation and computer science, as well as commonly used terms with a specific meaning in the context of this thesis.

**Citizen participation:** A set of practices aiming at giving citizens a certain degree of influence in the governance of a specific institution to which they are related (e.g., their city, their university, the parliament of their country). In this thesis, we will use the definition of citizen participation given by Baum [22], stating that citizen participation is “the involvement of citizens in public decision making”. This definition does not specify the degree of involvement of citizens, both concerning their decision-making power and the types of contributions they produce. Therefore, we will specify the type of citizen participation we study in each chapter of the thesis as the nature of contributions and the level of empowerment given to citizens may vary significantly between them.
**Consultation:** Consultation is a specific type of citizen participation in which citizens are asked by an institution to produce contributions on a given topic. Contributions can take various forms, including answers to a survey, textual proposals or videos. The specificity of consultation is the absence of decision power given directly to citizens. Consultations are particularly well-represented in contemporary citizen participation, with common examples being participatory budgeting or neighborhood councils.

**Citizen participation platform:** An online platform used in the context of a participatory initiative to let citizens produce contributions. These platforms are alternatives to more traditional structures for citizen participation, such as offline neighborhood councils.

**Proposal:** In the context of a citizen participation platform, a proposal is a text-based contribution which presents an idea, its objectives and potentially details its functioning or expected costs and benefits. Unlike comments, proposals do not reply to another contribution and can be read independently.

**Scale:** In this thesis, the scale of a citizen participation platform refers to the number of citizens involved and the number of contributions they produced. Typically, a large-scale participatory platform would aim at giving voice to the citizens of a large city or a country.

**PageRank:** The PageRank algorithm [125] is an algorithm aiming at measuring the importance of vertices in a directed graph. In this algorithm, after an initialization of the PageRank of each vertex to a given value, this value is re-computed for any vertex u using the following formula:

$$PR(u) = \sum_{v \in B_u} \frac{PR(v)}{|E^+(v)|}$$

With $B_u$ the set of vertices with at least one edge pointing towards u, and $|E^+(v)|$ the total number of edges outgoing from the vertex v.

The most well-known use of this algorithm is to rank web pages in a search engine based on the number of other pages containing links towards them. However, multiple other use cases have been found for the PageRank algorithm, such as the detection of influential users on Twitter [24].

**Collaborative editing:** The editing of a single document by several contributors, each of them contributing individually to the final result. Collaborative editing can be performed using various means, from joint writing of a document by two persons in the same room at the same time to online development of an open source software by tens of persons in multiple countries and over a long period of time. The CSCW Matrix (see Figure 2.1) illustrates the diversity of computer-based collaboration, including collaborative editing.
<figure><img src="image_4.png" /><figcaption>Figure 2.1: Johansen's CSCW matrix [80] – Image released under the public domain at https://commons.wikimedia.org/wiki/File:Cscwmatrix.jpg</figcaption></figure>

**Kappa score:** Cohen's kappa score [41] is a measure for the degree of agreement between two persons annotating the same dataset. The kappa score is a number lower or equal to 1, with a score of 1 expressing a perfect agreement between the two annotators, and a score lower than 0 expressing a lower agreement than expected by chance. The score is computed as follows:

$$ \kappa = \frac{p_o - p_c}{1 - p_c} $$

With $p_o$ the proportion of items labeled identically by both annotators and $p_c$ the proportion of items expected to be labeled identically by chance.

**Token:** In NLP vocabulary, a token is a part of a character string. More specifically, in this thesis, tokens are either words, subwords, numbers or punctuation marks. Each sequence of tokens also starts with a specific opening token and ends with another specific closing token.

## 2.2 Online citizen participation

With the focus of this thesis being on online citizen participation, we first have to present parts of the work done by other researchers on this field. First, we will briefly summarize elements of research on citizen participation itself to define its objectives and identified limits. Then, we will propose a presentation of state of the art in online citizen participation research will focus on two axes: existing analyses of online citizen participation itself or specific platforms, and means to improve contemporary platforms.

### 2.2.1 Citizen participation

Citizen participation aims at involving citizens in public policy making through a wide range of practices. Indeed, participation has been used with multiple objectives
<figure><img src="image_5.png" /><figcaption>Figure 2.2: Illustration of Parra *et al.'s* [128] workflow for participatory budgeting.</figcaption></figure>

depending on the type of process implemented and the degree of power given to citizens. In [27], Blondiaux briefly compares citizen participation experiences in Porto Alegre, which aimed at redistributing wealth and power to underrepresented citizens through participatory budgeting, and those in England, where the focus was on identifying the preferences of “public services users”. Other benefits of citizen participation have also been identified and are often used as arguments in favor of citizen participation. A first example of such argument is the capability of citizens to produce original knowledge based on their expertise or experience. Inclusion of this knowledge is considered as mandatory in contemporary societies where citizens are capable of questioning or supplementing the expertise provided by institutions [23, 110]. Another benefit of citizen participation is the opportunity for citizens to come together and collaborate in support of common causes, therefore reducing the loss in popularity of collective structures identified since the 1990s [135].

However, multiple limits have also been identified in citizen participation practices. The main issue is a tendency to under-represent minority and vulnerable groups. This issue has been linked to the tendency of deliberative democracy to encourage expressions considered as measured and rational and to disregard more radical and vehement stances [58, 112]. Additional explanations have also been proposed, such as the expected low impact of a participatory process on the final decision or the absence of practical means to encourage the participation of certain groups (e.g., children care services for late meetings) [27]. Finally, in the specific case of online participation, another group is under-represented: computer-illiterate persons, which highlights the need to maintain offline participatory initiatives.

In [128], Parra *et al.* propose different design spaces for computer-based elements in participatory budgeting based on a workflow they define. This workflow can be used to describe most citizen participation initiatives. Indeed, identifying in which steps of the workflow citizen input is requested in a given participatory process can be a helpful mean to describe it. Typically, a participatory process where citizens are given a high degree of control will request citizen inputs in most steps of the workflow, and especially the last ones. To give a better understanding of the different potential steps of a participatory process, we reproduce the schematic representation of this workflow in Figure 2.2.
### 2.2.2 Analyses of existing online citizen participation contexts

Online citizen participation has been the topic of multiple studies in computer science, especially in the CSCW and HCI fields of research as stated by Neli-markka [117]. These studies have been made on various types of participatory contexts, including online petitioning systems [74, 162, 178], participatory budget- ing [128, 182] or citizen sensing [68, 82]. Studies mentioned above on online peti- tioning identify non-linear correlations between the number of petitions for a given topic and the number of signatures they receive, analyze the behavior of “power” users in short and long terms to identify their differences with other petition sign- ers, and identify indicators of success of a petition as perceived by contributors. Concerning participatory budgeting, authors identify crucial motivators to keep cit- izens involved in recurring campaigns after the first year, and analyze the different practices and technologies leveraged in the different steps of contemporary online participatory budgeting to identify their flaws. Finally, research on citizen sensing presents the challenges faced by users of citizen-sourced data, including information overload and misinformation. In addition, it demonstrates the viability of citizen sensing for different topics, such as noise pollution monitoring.

Outside of the field of computer science, multiple works have studied online citizen participation and its different implementations. In her thesis, Laurent [90] proposes an in-depth analysis of the *République Numérique* participatory process from an information and communication sciences perspective. Her works describe the different stakeholders involved in this process and their behavior, from contribu- tors to the platform to media coverage. They also propose an analysis of the design of the participatory platform, examining its significance in the presentation of the debate and exploring the means through which participants can articulate and de- fend their perspectives [91]. Furthermore, her thesis proposes a thorough analysis of the impact of this participatory initiative on the law-making process and on the final text voted by the French parliament.

The large number of studies focused on online participation has led to the need for means to classify participatory contexts. Kelty et al. [84] propose a classifica- tion framework for citizen participation based on seven different dimensions. These dimensions are:

**Educative dividend:** The possibility given to citizens to learn how to to partici- pate effectively and to improve their knowledge on the topic of the participa- tory initiative.

**Goals and Tasks:** The possibility for citizens to help set the goals.

**Resource Control:** The capability for citizens to gain control or ownership over the inputs they produce and the means used to produce these inputs. Typi- cally, resource control can be given to citizens in an online participatory initia- tive through the use of an open source platform and the release of contributions as open data. However, certain participatory initiatives propose other types of practices such as the use of a platform designed with citizens [33, 86].

**Exit:** The possibility for citizens to disengage from the participatory process at any time and without any penalty.
**Voice:** The existence of means for citizens influence the outcomes of the participatory initiative and to speak back or criticize the said outcomes in a dialogue between citizens and the institution.

**Visible metrics:** The existence of relevant links between the inputs produced by citizens and the outcomes of the participatory process.

**Affective / Communicative Capacity:** The existence of tools for social interaction between citizens (e.g., discussion) and of a sense of belonging to the community of participants from an emotional point of view.

According to their descriptive framework, we consider the participatory platforms studied in depth in this thesis – namely, *République Numérique* and *Revenu Universel d'Activité* – to be strong on the **“Educative Dividend”**, **“Exit”**, **“Visible metrics”** dimensions while being weak on the **“Goals and Tasks”**, **“Voice”** and **“Affective / Communicative Capacity”** dimensions and in-between on the **“Resource Control”** dimension. This distribution is not specific to the two consultations studied here, as we find multiple other examples in online citizen consultations such as Decidim Barcelona<fnref n="1" />, Decide Madrid<fnref n="2" /> or New York City’s idea collection<fnref n="3" /> and participatory budgeting<fnref n="4" /> platforms.

Through a systematic literature review of empirical papers focused on citizen participation, Ianniello et al. [75] identify several main obstacles to participation and propose solutions to overcome them. The three families of obstacles identified are contextual factors (e.g., attitude of stakeholders), organizational arrangements (e.g., design of the participatory process), and process management patterns (e.g., quality of the collaboration between citizens). While most of the papers cited in the literature review are focused on offline citizen participation, we consider that many of the conclusions are also relevant for their online counterparts. Related conclusions drawn in this thesis especially aim at improving the means offered by participatory platforms to help citizens collaborate, therefore reducing the third obstacle identified in [75] for citizen participation.

Naranjo-Zolotov et al. [116] identify citizens’ motivators in the context of a Portuguese online participatory platforms. Using a questionnaire sent to contributors, they identify that social capital<fnref n="5" /> is a crucial motivator for the engagement of citizens with participatory platforms. They also propose recommendations to help citizens engage with a participatory platform in the long term. Their methodology based on the identification of individual motivators could be used to understand the objectives of citizens in participatory platforms and in particular their motivators with regard to collaboration. Identification of such motivators could be complementary to our work to identify when collaboration is suitable and how it could be implemented in citizen participation contexts.

Glaas et al. [63] propose an analysis of contributors’ perception of a specific online participatory platform in Sweden. Their work is based on focus group interviews

<footnote n="1">¹https://www.decidim.barcelona/</footnote>

<footnote n="2">²https://decide.madrid.es/</footnote>

<footnote n="3">³http://ideas.pbnyc.org/</footnote>

<footnote n="4">⁴https://www.participate.nyc.gov/</footnote>

<footnote n="5">⁵In the paper cited, the concept of “social capital” is leveraged using Putnam’s definition, i.e., “connections among individuals — social networks and the norms of reciprocity and trustworthiness that arise from them” [135].</footnote>
with contributors, semi-directed interviews with administrators and a thematic analysis of contributions published on the platform. They identify that the main pitfalls of the platform for contributors are not directly related to its functionalities but rather to the design of the participatory process itself. While our approach differs from theirs – we focus on questions related to the creation of the platforms while they study the experience of contributors –, we consider their findings to be extremely relevant to improve online participatory initiatives independently from their technical basis. While the inclusion of citizens in the design of platforms has already been experimented [33], Glaas et al. [63]'s work highlights the importance of leveraging citizens' experience with the different online participatory platforms to improve contributors' engagement.

### 2.2.3 Improving online participatory platforms

Studying the behavior of contributors and their interactions in citizen participation platforms is crucial to identifying potential issues in the design of online participatory initiatives. However, other research has proposed ways to directly improve these platforms based on the issues previously identified. Schroeter [147] proposes to improve citizen participation at the local scale through the use of public interactive devices installed in Brisbane, Australia. The devices displayed ideas proposed by citizens and information on how to submit new proposals using SMS or Twitter, therefore improving the range of possible contexts for citizen participation at local scale. In similar local-scale citizen participation contexts, Yu and Cai [181] suggest enhancing the understanding of citizens and platform administrators by adding geographical annotations to contributions. Kriplean et al. [87] propose to modify the types of contributions produced by citizens, asking them to create “pro” or “con” arguments on given issues rather than proposals. Authors identify that such types of human-computer interface tends to allow citizens to form more balanced opinions, acknowledging potential opposing arguments. Cantador et al. [34] propose identifying controversial proposals based on different statistical metrics. Their study allows them to identify different controversies types and highlight a lack of interest for certain controversial proposals. However, these papers propose to improve participatory platforms based on elements outside citizens’ textual content. We consider that, in platforms allowing the creation of text-based contributions, analysis of these contributions is particularly useful to reduce information overload and give access to the main points of view and arguments in a debate.

Arana-Catania et al. [13] propose utilizing machine learning and NLP to improve citizen participation systems. Their exploratory study focuses on the relevance of machine learning to improve citizen participation platforms based on four different use cases. These use cases are the summarization of comments using text generation, the clustering of users using their topics of interest, and the identification of similar proposals using automated tagging or finding the most representative proposals for a given topic. NLP analysis of contributions can be an interesting mean to help reduce the information overload identified in participatory platforms [38, 132] and to foster citizen collaboration at large scale. Indeed, their user-centric metrics for assessment of their use cases (e.g., time to perform a given action with or without their tool) show that the implementation of their tools on the “Decide Madrid” platform drastically facilitate the retrieval of information for the different proposed
scenarios. We consider that the tools they propose could help citizens regroup to
create collective contributions based on the similarity of their proposals or user
clustering.

Athanasopoulos [16] introduces a framework for online direct democracy with a focus on privacy and scalability. Their framework allows the organization of votes on discussion topics and is based on the representation of participants as vertices in a distributed social network. The proposed network architecture allows both achieving better scalability in the number of participants than a centralized one, and avoiding the existence of a central authority owning the data. This framework could be implemented in online participatory platforms to ensure the privacy of participants and scalability up to the country's population. However, it is necessary to extend the means of participation offered to a wider scope than the sole (dis)approval of proposals by implementing features supporting the other phase of the generic workflow presented in [128].

The development of online citizen participation outside of dedicated platforms
has been the subject of prior studies. Angarita et al. [9] propose a middleware
to help citizens interact with one another and with other relevant entities (e.g.,
connected objects, institutions, etc.) through any social network and to identify
and solve public issues. Saldivar et al. [143] analyze the behavior of contributors on
a city-scale online participatory initiative where citizens were allowed to contribute
both using a dedicated platform and a Facebook group, with the content posted
on one of these platforms being mirrored on the other. However, their results show
that even though the existence of this Facebook-based version of the consultation
increased the number of users, a vast majority of the contributions were still made
using the dedicated platform. Therefore, we consider that dedicated platforms are
still the most relevant place to study online citizen participation and that their
technical improvement is a key factor to involve citizens in public decision making
more efficiently. Moreover, we stress a major advantage of platforms dedicated
to citizen participation, especially those with open-source software, which is the
possibility for the organizing institution to implement new features for contributors
or administrators (e.g., collaborative editing of contributions) independently from a
third-party actor – in Saldivar et al. ’s case [143], Facebook.

## 2.3 Large-scale online collaboration

The concept of online collaboration at large scale is largely used in this thesis.
Moreover, our findings in Chapter 4 lead us to consider online collaborative editing
(CE) as a crucial mean to let citizens produce common contributions. Indeed,
(computer-supported) CE systems allow people to work (online) on a common task,
and therefore simplify the creation of projects of all sizes. Online collaboration
also allows contributors from almost everywhere to participate without having to be
physically present at a given place, neither at a given time. Therefore, we propose
a summary of the two types of collaborative editing we differentiate in this thesis:
asynchronous and synchronous editing.
### 2.3.1 Asynchronous editing systems

In an asynchronous CE system, updates are explicitly shared by their creator and received by other participants upon request. This approach allows users to perform major updates to a document without compromising the other contributors' work. However, this challenges the automation of conflict resolution, which is generally handled by the users so that the document remains coherent with all the conflicting versions. This then requires a quite high level of computer literacy from the contributors who need to understand the issue of conflicting updates.

CE systems have taken various shapes over time, from sharing document updates via emails to massive online collaboration platforms, like GitHub or Wikipedia. They have also been used in a wide range of situations, both in software development and in natural language document writing.

In the context of software development, the most common tools for asynchronous editing are Version Control Systems (VCSs), which are defined in [37] as “*a system that records changes to a file or set of files over time so that [one] can recall specific versions later*”. They are frequently preferred to synchronous file editors for various reasons:

* It allows users to gain full control over their replica of the code they are working on. Assuming that the code runs without issues on the local machine when an update is received, this ensures that any bug or error encountered during development has been caused by subsequent changes made by the user and, therefore, simplifies the debugging process.

* Since updates in software development are typically bug fixes, new features, or improvement of existing ones, adding a single character or line is often not significant enough to be shared with other contributors, and may even create unpredictable behaviors and errors. The possibility to create updates, and label and share them when users want, gives contributors a higher control over the process.

* In the specific case of open source software development, habits and customs are shown to have a strong impact in certain communities [61, 152] and thereby influence the choice of the hosting platform for a software development project. Furthermore, the fact that the asynchronous GitHub, one of the biggest online storage platforms for code repositories, claims that 20.5 million new users have created an account in 2022 [121], suggests that choosing GitHub potentially gives greater visibility to the project.

Asynchronous editing systems for software development can be suitable tools for citizen participation since they allow the creation of *branches* (i.e., alternative versions) within a single project. This feature, when applied to participation, allows citizens to collectively create several proposals –at different stages of their development– for a same project, to fully develop them and then to choose the most suitable alternative.

Asynchronous CE systems also have a strong presence for the CE of documents in natural language, mainly in wiki engines<fnref n="6" />. Indeed, even though the concept of wiki

<footnote n="6">⁶Collaborative software running a wiki platform</footnote>
is not intrinsically linked to asynchronous editing, it is clear that the most popular wiki platforms use this approach. This tendency can be explained by the fact that the MediaWiki engine, which powers certain of the most popular wiki platforms implements an asynchronous protocol for sharing updates. The MediaWiki engine, which is used by platforms such as Wikipedia, FANDOM or wikiHow, allows users to edit the content of an article on a page separate from its main view and then send the changes to the server as a revision, with a description of the proposed changes. A change history allows tracking of the different revisions of a document and to possibly restore previous versions or remove the impact of an intermediary one. The efficiency of wikis as a tool for collaboration and knowledge aggregation has been studied in various contexts, like school projects [115, 158], teaching [119, 127, 169] or corporate knowledge management [72]. However, the asynchronous approach has sometimes been seen as an obstacle to collaboration in certain contexts, like collaborative lesson development among pre-service teachers [124].

Overall, asynchronous editing systems tend to be the most appropriate choice when the object of the collaboration requires one of the following functions:

* **Need for explicit versioning:** In many situations, the existence of labeled or fully described versions of a project is mandatory –e.g., in order to trace the history of changes of a paragraph or a software feature. In these cases, choosing asynchronous editing can be an advantage since these systems require contributors to explicitly mark the end of a contribution, and therefore simplify their labeling.

* **Possibility to execute or compile the edited documents:** When the edited document contains computer code, which is meant to be interpreted or compiled, it is often mandatory to adhere to its syntax and semantics to avoid errors. Consequently, using an asynchronous CE system, the developer gains full control on the version of the project they work on, therefore avoiding the risk of compiling it while someone else is making changes which may create syntax and/or semantic errors.

* **Public document edition:** In cases where write access to a document is not restricted to a specific group of users (for example, most wiki websites allow everybody to edit their pages), asynchronous editing may be the only viable option. Indeed, in such a case, there is a non-negligible risk of vandalism [3, 151] and of edit wars [159, 180]. These problems are likely easier to solve if the contributions are large updates rather than atomic changes, since a detailed version history simplifies the recovery of a former undamaged version. However, certain algorithms for synchronous editing that allow the identification and reversion of long updates [160, 173] can also be considered as a solution for such issues.

### 2.3.2 Synchronous editing systems

To the best of our knowledge, synchronous CE has been implemented for the first time at the Augmented Human Intellect (AHI) Research Center of Stanford Research Institute, in 1968 [55]. Changes made using a synchronous CE system are sent to the participants as soon as they are done or after a short delay. This method allows the
users to see each others updates as they are issued, which facilitates collaboration. Moreover, since these systems are meant to handle simultaneous work on the same document, they are able to resolve writing conflicts automatically, which lowers the level of computer literacy required to use such systems. However, conflict resolution requires specific algorithms designed to maintain consistency among the different replicas of the document edited by each user. These algorithms are grouped into two categories: (1) *Operational Transformation (OT)-based algorithms* [54] and (2) *Conflict-free Replicated Data Types (CRDT)-based algorithms* [153]. We will not detail the technical differences between these two categories here since, in this thesis, synchronous editing systems can be considered as black boxes. We will simply outline the main characteristics of these two categories to help differentiate them.

OT is a class of algorithms designed to maintain consistency over multiple nodes handling replicas of the same object (a database, a document, an array, etc.). It is based on the control and transformation of concurrent operations to achieve the eventual consistency of replicas. It has been designed to solve the potential inconsistency produced by two operations that can not be causally ordered. In order to detect whether two operations are concurrent or not, several methods can be implemented; the most frequent of them is the use of a server assigning timestamps to operations, allowing sites to reconstruct the history by themselves. In an OT-based system, the positions at which received operations should be applied are transformed on each site, based on concurrent local operations. However, most of these algorithms have been proven incorrect during the past years [76] since they do not achieve convergence in certain cases.

As an alternative to OT, CRDT has been defined in [153] as a data structure allowing strong eventual consistency between nodes of the network without any coordination mechanism. Systems that implement this structure send data with an overhead that enables every node in the network to place elements (e.g., characters of a text) at the same position. CRDTs do not need a central server by design and are therefore more appropriate in contexts where there is no trusted entity able to store and process contributions. This property comes from the commutativity of operations, or of the merging function in the case of state-based CRDTs, allowing them to be executed independently of the context with the same result. However, some of the proposed optimizations may need a centralized architecture.

Both of the aforementioned coherence algorithm classes have specific implementations for synchronous CE. However, the proven correctness of CRDTs is a clear argument in their favor.

Nowadays, synchronous editors are mostly used for editing documents in natural language through editors such as Google Docs, Etherpad or MUTE [118]. These editors allow users to create and edit documents like they would do using a regular text processor plus the specific features proposed by synchronous CE. As shown in [123], these editors give users new means of collaboration using recognizable patterns of work. However, the direct sharing of contributions to a document can also be the source of inconveniences for the users, from the complexity induced by collaboration rules of a group to the difficulty to share contributions when they are yet not finished and do not fully represent the ideas their authors want to share [170]. The latter can be a major obstacle for synchronous collaboration in citizen participation circumstances, especially if contributors are supposed to share ideas or knowledge.
Other synchronous CE systems exist, such as synchronous wiki engines (Nuclino<sup>7</sup>, Slite<sup>8</sup>, Confluence<sup>9</sup>, etc.). These systems, like their asynchronous counterparts, allow users to modify the content of a web page directly through its interface. Their functioning is very similar to that of document editors mentioned above. Their main advantage is to avoid asking users to do conflict resolution since it is done automatically. However, they do not allow the creation of a semantically labeled change history.

Finally, we can mention the fact that synchronous editing systems have been proposed for software development [65, 69]. They generally allow users to see each other's code as they are writing it but it is not run by others' computers as long as it is not syntactically correct or marked as ready to use by the author. However, these systems are nowadays rarely used, abandoned or still in prototype stage.

Summarizing, the main situations where synchronous collaborative editors tend to be preferred to their asynchronous alternatives are:

* **Need for a solution that is easy to use:** In cases where the contributors are not supposed to have a high degree of computer literacy, reducing the difficulty of use is of paramount importance. Therefore, automated sharing of contributions and conflict resolution are important features in this kind of situation. Moreover, using a synchronous editor guarantees to the user that their contributions are applied to the most recent version of the project.

* **Necessity for contributors to be able to do joint writing:** In certain situations –e.g., when a user is supposed to correct spelling mistakes or when a text is supposed to reflect two persons’ perspectives on a subject–, synchronous editing is the most viable option available. Apart from the possibility to do joint writing, synchronous editing also gives contributors the possibility to send feedback to a co-author as they are writing.

## 2.4 Knowledge graphs

Knowledge Graphs (KGs) represent facts in the form of nodes and relationships [70, 96]. They use a graph-based data model to capture knowledge in scenarios that involve integrating, managing and extracting data from diverse sources [120]. The two main graph models are RDF [103] and Property Graphs [11]. RDF is a W3C standard RDF for data interchange on the Web. Nodes are resources with a unique identifier on the Web. Edges link the nodes by building subject–predicate–object expressions. Property graphs are directed labeled multigraphs in which nodes and edges can have properties in the form of key-value pairs. Nodes represent entities and edges represent relationships between those entities. Both RDF and Property Graphs have their advantages and disadvantages, depending on the use case [6, 53].

The creation of a KG from text involves the use of Natural Language Processing (NLP) techniques, including Named-Entity Recognition (NER) [113] and Named-Entity Linking (NEL) [154]. NER focuses on identifying and categorizing entities within text, such as people, organizations, and locations, among others. NEL, on

<footnote n="7"><sup>7</sup>https://www.nuclino.com/</footnote>

<footnote n="8"><sup>8</sup>https://slite.com/</footnote>

<footnote n="9"><sup>9</sup>https://www.atlassian.com/software/confluence</footnote>
the other hand, connects the recognized entities to entities within a knowledge base. Several methods for constructing domain-specific KGs have been developed across a range of domains, including fashion, medicine, and more [21, 141, 140, 161].

Barroca et al. [21] applied Named Entity Recognition (NER) and Named Entity Linking (NEL) techniques on product textual descriptions to enhance a fashion-related KG. The process involved identifying entities, such as materials used in the product, within the descriptions and linking them to their corresponding, unique nodes in the KG. Rincon-Yanez et al. [141] proposed a methodology for creating KGs related to novels. This methodology involves using NLP techniques such as NER and NEL to generate simple triples as a starting point, and then enhancing them using external open knowledge sources, such as DBpedia, to build a comprehensive KG. This methodology was tested on a novel, resulting in the extraction of characters and their relationships. In a similar vein, Alpizar-Chacon and Sosnovsky [7] proposed a solution which automatically labels book's index item in relation to the main subject of a textbook area. These index concepts can be associated with the central themes of the book, common concepts, topics related to but distinct from the book's subject area, or unrelated to the book's subject area altogether.

Reklos and Meroño-Peñuel [140] developed an approach for identifying causal relationships in medical publications. Their approach involves detecting causal sentences, extracting entities, and using this information to build a KG that represents the causal relationships. For example, a node representing the cause of an illness would be connected to a node representing the illness.

Another relevant application of KGs is the analysis of parliamentary debates, as demonstrated by Tamper et al. [161]. They proposed a method for constructing and enhancing KGs of plenary debates in the Parliament of Finland. The methodology involves preprocessing the text, including lemmatization to normalize the text and subject indexing to describe the text succinctly, due to the particularities of the Finnish language. NER and NEL techniques are then applied to extract named entities and link them to the KG.

We consider knowledge graphs to be a great mean to improve online citizen participation. For example, representing online participatory initiatives as knowledge bases would allow an enrichment of the contributions using preexisting external resources.

## 2.5 NLP and machine learning

The NLP field of research has implemented different methods which can be leveraged to improve certain issues faced by large-scale online citizen participation. In this section, we define certain key notions related to the use of NLP in this thesis, and particularly in Chapter 5. We will also present previous works on NLP and machine learning research used as background work for this thesis.

**Textual Entailment Recognition / Natural Language Inference:** Textual Entailment Recognition (TER), also known in the literature as Natural Language Inference (NLI), is the task of deciding, given two texts, a premise and a hypothesis, if: i) the hypothesis can be inferred from the premise (entailment); ii) or if the negation of the hypothesis can be inferred from the premise (contradiction); iii) or if neither of the two situations is true (neutral).
Several annotated datasets have been proposed for the task, such as the Stanford Natural Language Inference (SNLI) Corpus [30] and the Cross-Lingual NLI Corpus (XNLI) [45]. We note that on the SNLI task, inter-annotator agreement varied between moderate and substantial. This is a consequence of the task requiring a common understanding of language and background knowledge. Moreover, the exact definition of what can be inferred from a piece of text is left to the understanding of each annotator. The GLUE [168] and SuperGLUE [167] benchmarks, two standard benchmarks for general-purpose language understanding systems, include textual entailment as a challenge. The best performing systems on the benchmarks are Transformer-based models, such as BERT [52], RoBERTa [97] or PaLM [39]. Research has shown that the performance of classifiers on such datasets is misleading and textual entailment remains challenging [67, 106]. In many cases, the classifiers learn simple heuristics to predict a class, such as token overlap between entailed sentences or the presence of negation for contradictory sentences.

**Analyzing the textual content of participatory platforms.** Participatory platforms are a meaningful way of engaging citizens and finding innovative solutions to societal struggles. One of the success criteria for collaborative platforms is the number of participating citizens. However, decision-makers, such as governments, often do not have the means to deal with high volume and complex citizen inputs [38]. In addition, citizens might struggle to assess if their contributions bring new elements to the conversation and might be overwhelmed by the other contributions [132]. Several techniques have been proposed in the literature to alleviate this problem. In [13], the authors propose using topic modeling to generate tags for each proposal to facilitate the search by keywords, extractive summarization for the comments associated with proposals, and user clustering based on the contributions. In [19], proposals are clustered using the Word Moving Distance, while in [14] comments are summarized using abstractive summarization models. In [149, 150], the authors propose extracting arguments from user input. In [150], the authors create a taxonomy of arguments and propose a rule-based approach using syntactic patterns to extract arguments. In a follow-up work [149], the argument extraction module is integrated into a keyword search system. Different from previous works, in Chapter 5 of this thesis, we focus on a more fine-grained understanding of user input by retrieving entailed and contradictory proposals, which should be grouped or reconciled. In addition, we use state-of-the-art techniques for natural language processing, and we evaluate our approach on two major national citizen consultations.

**Semantic Textual Similarity.** Semantic Textual Similarity (STS) measures the degree of semantic equivalence between sentences. This task is fundamental in NLP and has essential applications in measuring the success of other tasks such as machine translation, summarization, or question answering. Semantic textual similarity is related to textual entailment and paraphrase detection but differs from these two tasks in that it does not perform a binary classification but captures the degree of similarity. Several supporting datasets have been proposed [36, 104, 111]. Similarity is usually measured on a scale of 0 to 5. Each gradation is explained to human annotators: completely dissimilar sentences are given 0; equivalent sentences are given 5; and, for example, sentences that are roughly equivalent but have important
different or missing details are given 3. There is no clear guideline for dealing with contradictory sentences, which may well receive high similarity scores [36]. One of the most successful models for computing textual similarity are siamese Transformer networks [137].

## 2.6 Conclusion

This chapter lays the foundation for the rest of my Ph.D. thesis by introducing key concepts and providing background information relevant to these concepts. The first section highlights certain key definitions used in all or parts of this document. We then propose a presentation of preexisting analyses of online citizen participation from the HCI and CSCW fields of research, as well as a summary of the means proposed to solve already identified issues in participatory platforms based on state-of-the-art technologies. We also present key notions to understand online collaboration with a focus on the synchronicity of collaborative systems. Then, we summarize a series of concepts and preexisting works from knowledge graphs research. Finally, we conclude this chapter with a presentation of NLP and machine learning concepts and background works used in the following parts of this thesis.

The following chapter of this thesis will be focused on the presentation of the first contribution of my Ph.D.: an analysis of a specific citizen consultation platform based on the interactions between its contributors and their contributions.
# Chapter 3

# Analysis of an existing participatory platform

## Contents

<table><tr><td>3.1</td><td>Introduction</td><td>49</td></tr><tr><td>3.2</td><td>The République Numérique citizen consultation</td><td>51</td></tr><tr><td>3.3</td><td>Does the platform influence how users contribute?</td><td>53</td></tr><tr><td>3.3.1</td><td>Analyzing the distribution of contributions across users</td><td>53</td></tr><tr><td>3.3.2</td><td>Analyzing the factors influencing the contributions</td><td>54</td></tr><tr><td>3.4</td><td>How to mitigate the influence of the most active users?</td><td>57</td></tr><tr><td>3.5</td><td>Does the positioning of Proposals have an impact on participation?</td><td>59</td></tr><tr><td>3.6</td><td>Building an Online Public Consultation Knowledge Graph</td><td>60</td></tr><tr><td>3.6.1</td><td>Topic modeling</td><td>60</td></tr><tr><td>3.6.2</td><td>Entity Recognition and Linking</td><td>61</td></tr><tr><td>3.6.3</td><td>Resulting Knowledge Graph</td><td>63</td></tr><tr><td>3.7</td><td>Threats to validity</td><td>63</td></tr><tr><td>3.8</td><td>Related Work</td><td>64</td></tr><tr><td>3.9</td><td>Conclusion</td><td>66</td></tr></table>

The content of this chapter has been published in the FairWare'22 workshop proceedings [1] under the name "Detecting Obstacles to Collaboration in an Online Participatory Democracy Platform: A Use-case Driven Analysis", with the exception of Section 3.6. This section is based on the results of our paper "Building Online Public Consultation Knowledge Graphs" [2], written with Sonia Guehis and Rafael Angarita and published in the Text2KG'23 workshop proceedings.

## 3.1 Introduction

Participatory platforms address various applications, e.g., participatory budgeting [71, 128], releasing open data for civic purposes [28, 94], gathering and processing
the opinions of large groups of citizens [134], and citizen consultation and deliberation, to name a few. In this chapter, we focus on an example of the fourth case: the "République Numérique" (Digital Republic, *RepNum* for short in the following) online consultation (§ 3.2). Online consultations aim at allowing citizens to directly express their opinions on a particular topic –in our use case, the different articles of a bill on France's digital transformation– outside of any electoral context. They are frequently considered as a way to improve the participation of citizens in a representative political system although they are non-binding for the organizing institution. Technically, online consultation platforms can be seen as CSCW platforms allowing users to collaboratively edit a set of independent proposals by adding new elements or by discussing previously added ones. They generally implement at least the three following forms of contributions:

1. User A submits a proposal, detailed or vague, to the platform.

2. User B comments on a proposal formerly submitted by User A.

3. User B votes for, against, or neutrally, on A's proposal.

The *RepNum* platform has already been the subject of several academic papers, notably in the French-speaking social science community. Existing analyses focus mainly on the discourse accompanying the implementation of the platform [5] and on the interaction possibilities offered to citizens by the platform design [91]. However, to the best of our knowledge, there does not exist any publication focused on the technical analysis of the impact of platform design on the way users contributed to the *RepNum* consultation. There exist technical analyses of the user contributions to similar platforms such as Decidim [12] or more general collaborative systems like Google Docs [123]. These studies focus on the inference of collaboration models from the users' interactions. Instead, we aim at eliciting recommendations mitigating bias in the platform's design to enhance the engagement of citizens in online participatory platforms. Our objective is to help build more equitable participatory platforms based on the data produced by previous experiences. To do so, we analyze the users' contributions and interactions in the *RepNum* platform. This specifically leads us to investigate the following Research Questions out of which we draw recommendations for the design of participatory platforms:

**RQ1:** How is the attention distributed among proposals and are platform-related factors at the origin of this distribution? (§ 3.3)

**RQ2:** How do the most active contributors use the different types of contributions (proposal, modification, argument, source, vote) and how can we mitigate their influence on the consultation? (§ 3.4)

**RQ3:** What is the impact of the presentation of proposals on the participation? (§ 3.5)

Moreover, to mitigate biases identified in this contemporary participatory platform's design, we propose to improve the range of tools available to citizens and platforms administrators to find specific proposals. To do so, we propose to build a knowledge graph of public consultations in § 3.6. The graph proposed is generic
<table><caption>Table 3.1: Possible types of replies for a contribution.</caption><thead><tr><th>Can be replied with...<br>Initial contribution</th><th>Argu-ment</th><th>Modifica-tion</th><th>Source</th><th>Vote</th></tr></thead><tbody><tr><th>Argument</th><td></td><td></td><td></td><td>×</td></tr><tr><th>Modification</th><td>×</td><td></td><td>×</td><td>×</td></tr><tr><th>Proposal</th><td>×</td><td>×</td><td>×</td><td>×</td></tr><tr><th>Source</th><td></td><td></td><td></td><td>×</td></tr></tbody></table>

and can be used on most online citizen consultations. Its contributions are the following: i) the enrichment of an online public consultation data with linked data to improve the contextualization of texts, facilitate its integration with other linked data sources including other consultations, increase transparency and accountability, and enhance decision making; ii) the analysis of proposals to infer their topics for better organization and understanding of the information contained within the consultations. By categorizing consultations based on topics, it can be easier to identify trends and patterns in public opinion and to compare and contrast different viewpoints.

All code used in the production of the analyses is available at https://github.com/WilliamAboucaya/repnumbehavioranalysis for Sections 3.3, 3.4 and 3.5, and at https://github.com/WilliamAboucaya/repnum-kg for Section 3.6.

We then analyze the threats to validity (§ 3.7) and position our contributions with respect to related work (§ 3.8). A summary of our contributions and perspectives for future work conclude the chapter.

## 3.2 The République Numérique citizen consultation

The consultation for the *RepNum* bill was held from 09/26 to 10/18 2015 and primarily took place through the dedicated online platform [43]. The main objective of the (essentially online) consultation was to allow contributors to propose amendments –called *Proposals*– to the bill before its examination by the French Parliament, while giving no guarantee that the amendments would be integrated.

The *RepNum* platform builds upon the proprietary solution for online consultations of the Cap Collectif company<fnref n="1" />. Using the *RepNum* platform, contributors register using their real name or a pseudonym. They also specify their *profile*, that is, whether they are *citizens*, *institutions*, *non-profit* or *for-profit* organizations. Over the last few years, other consultations have taken place in France, particularly concerning vaccination [95, 171], and in other countries such as Australia [32] and Canada [98]. However, we chose to analyze the *RepNum* consultation because it received a large number of contributions, and the platform allows more diverse types of contributions (see Table 3.1) compared to its main alternatives, e.g., CONSUL [47] or Decidim [51]. Distinguishing features include the addition of a source (e.g., a newspaper article) to a preexisting proposal, and the amendment of a proposal through a modified version. We argue that such functions are vital in a collaborative

<footnote n="1">¹https://www.cap-collectif.com/</footnote>
<table><caption>Table 3.2: Number of contributions of all types by user profile.</caption><thead><tr><th>PROFILE</th><th>USERS</th><th>PROPOSALS</th><th>MODIFICATIONS</th><th>SOURCES</th><th>VOTES</th><th>ARGUMENTS</th></tr></thead><tbody><tr><td>Citizen</td><td>13,492<br>62.99%</td><td>400<br>57.803%</td><td>950<br>68.493%</td><td>369<br>88.916%</td><td>112,322<br>76.080%</td><td>4,627<br>77.245%</td></tr><tr><td>Non-profit<br>organization</td><td>393<br>1.831%</td><td>110<br>15.896%</td><td>157<br>11.319%</td><td>14<br>3.373%</td><td>1,955<br>1.324%</td><td>291<br>4.98%</td></tr><tr><td>Institution</td><td>199<br>0.927%</td><td>22<br>3.179%</td><td>47<br>3.389%</td><td>11<br>2.651%</td><td>1,141<br>0.773%</td><td>62<br>1.035%</td></tr><tr><td>For-profit<br>organization</td><td>154<br>0.717%</td><td>13<br>1.879%</td><td>27<br>1.947%</td><td>4<br>0.964%</td><td>1,043<br>0.706%</td><td>66<br>1.102%</td></tr><tr><td>Government</td><td>1<br>0.005%</td><td>30<br>4.335%</td><td>0<br>0%</td><td>0<br>0%</td><td>0<br>0%</td><td>0<br>0%</td></tr><tr><td>Others</td><td>7,225<br>33.661%</td><td>117<br>16.908%</td><td>206<br>14.92%</td><td>17<br>4.096%</td><td>31,176<br>21.117%</td><td>944<br>15.760%</td></tr><tr><td>Total</td><td>21,464</td><td>692</td><td>1,387</td><td>415</td><td>147,637</td><td>5,990</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Figure 3.1: Organization of proposal categories with examples of categories from the *RepNum* bill (translated from French).</figcaption></figure>

and confrontational situation to help solve potential disagreements and help everyone form an informed opinion on a particular topic, as described in [101]. We further highlight that the functions offered by the *RepNum* platform match the ones of the generic interaction workflow supporting participatory budgeting presented in [128] (see Figure 2.2).

Statistics indicate that the consultation has reached a diverse audience larger than only experts in IT and companies of the field [42, 77, 78]. However, the way users have contributed varies greatly depending on their profile. Table 3.2 illustrates how the different types of contributions are distributed among the different user profiles. For example, it emphasizes that certain profiles, particularly non-profit organizations and the French government, have published many more *Proposals* than others per individual, especially citizens.

All the contributions to this consultation and related metadata are available online as open data at https://www.data.gouv.fr/datasets/consultation-sur-le-projet-de-loi-republique-numerique. This enables us to conduct an in-depth analysis of how users engage based on their profile and types of contributions, from which we are able to derive platform pitfalls and recommendations for the platform design to enhance the user engagement. Contributions relate to one
of the bill *categories* (i.e., the specific part, chapter, and section, according to the overall structure of the bill depicted in Figure 3.1). As detailed in Table 3.2, the dataset gathers 156,121 contributions from 21,464 unique authors. However, the dataset omits a key feature of the platform: it pins the *Proposals* from the government –i.e., part of the initial law bill– on top of every section of the consultation, while the government profile is of type "institution" in the dataset. Thus, to distinguish the government *Proposals*, we pre-processed the dataset to replace their profile "institution" by "government".

# 3.3 Does the platform influence how users contribute?

## 3.3.1 Analyzing the distribution of contributions across users

In order to identify which contributors have received the most attention, we apply the PageRank algorithm [125] that measures the importance of vertices in a directed graph through the number of edges pointing towards them. Various papers introduce PageRank adaptations to detect influential users in social networks, while accounting for the specifics of the underlying platform [24, 174]. We specifically use the weighted PageRank algorithm [179] that fits well the interactions implemented in the *RepNum* platform. We built four weighted directed graphs representing the interactions between contributors using the four types of replies (i.e., *Argument*, *Source*, *Modification*, *Vote*, interactions between these contributions are presented in Table 3.1) to a contribution –*Proposals*– are ignored since they cannot follow from another contribution. We distinguish the different types of contributions to avoid giving the same weight to two different contributions that do not have the same purpose and effort-requirement (e.g., a 1-click vote *vs* a detailed argument). It also allows us to analyze if the PageRanks of a user correlate for different types of contributions (see § 3.3.2). Hence, each of the four graphs focuses on a single type of contribution where: the vertices are the users/contributors to the platform, and a directed edge from a contributor *A* to a contributor *B* represents a reply contribution of the chosen type from *A* to a contribution from *B*. Each graph is defined as $G(V, E)$ with *V* (resp. *E*) denoting the set of vertices (resp. edges). We note $E^+(v)$ the set of edges outgoing from a vertex *v*. Then, $w: E \to \mathbb{R}$ returns the weight of a given edge where the weight of an edge *e* outgoing from a vertex *v* is computed as:

$$ \forall v \in V, e \in E^+(v) : w(e) = \frac{1}{|E^+(v)|} $$

After applying the PageRank algorithm to the four graphs, we filtered each one of them by removing the users who did not publish any contribution that can generate replies of a given type from the associated graph (e.g., a user who did only publish *Sources* cannot receive an *Argument*). We then ordered the PageRanks in ascending order (see Figure 3.2). For each graph, we can clearly identify a small minority of contributors whose activities generate the most replies, as represented by an exponential growth of the PageRank. We argue that the existence of this small minority whose *Proposals* centralize the vast majority of replies of a specific type tends to alter the nature of the consultation for most users. Indeed, since most users receive little to no feedback to their contributions, they could disengage from the consultation. The satisfaction survey conducted by Cap Collectif[44] supports
<figure><img src="image_7.png" /><figcaption>Figure 3.2: PageRank of users in ascending order.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 3.3: PageRanks correlation for different reply types.</figcaption></figure>

this claim as it indicates that 81.4% of the respondents have visited the platform less than 5 times. This may also prompt users to change their behavior, from proposing new articles for a bill project to commenting and approving preexisting *Proposals*. This leads us to make the following recommendation:

**REC. 1.** To give voice to a wider part of the audience, we recommend to highlight the newest *Proposals*, in a way similar to the chronologically sorted layout proposed by platforms such as *StackOverflow*, to improve the share of initial *Proposals* that receive attention and feedback. The *RepNum* platform already supports sorting the *Proposals* of a section chronologically; however, this is not the default selection.

### 3.3.2 Analyzing the factors influencing the contributions

After obtaining the different PageRanks of the consultation, we investigate whether the highlighted centralization of contributions is directly related to contributors with
high PageRanks and their individual influence, or depends on the *Proposals* themselves, independently of their authors. To do so, we first investigate whether or not the PageRank of a user for a given type of contribution is correlated to their PageRank for the other types. This correlation allows us to know if the contributions of two different types are centralized around the same initial contributors, and therefore to identify if the users who tend to receive the most replies of a given type also receive the most replies of other types. Let:

* $type_a$ and $type_b$ denote types of contributions among {Argument, Modification, Source, Vote};

* $PR_{type_a}$ (resp. $PR_{type_b}$) denotes the list of users sorted by ascending order depending on their PageRanks for $type_a$ (resp. $type_b$) contributions -the list refers to all the unique users contributing to the consultation;

* $r \in [0, 1]$ denotes the share of users taken from the end of the lists $PR_{type_a}$ and $PR_{type_b}$ (i.e., $r = 0.05$ means we compute the similarity of the most active 5% contributors for $type_a$ and $type_b$ contributions); and

* $PR_{type_a,r}$ (resp. $PR_{type_b,r}$) denotes the sublists of $PR_{type_a}$ (resp. $PR_{type_b}$) obtained after picking the $r$ share of the highest PageRanks in both lists.

The similarity ratio $s_{PageRank}$ provides the level of similarity between $PR_{type_a,r}$ and $PR_{type_b,r}$ (i.e., at $s_{PageRank} = 0$, none of the users in $PR_{type_a,r}$ are in $PR_{type_b,r}$, and at $s_{PageRank} = 1$, both lists are identical):

$$s_{PageRank} = \frac{|PR_{type_a,r} \cap PR_{type_b,r}|}{|PR_{type_a,r} \cup PR_{type_b,r}|}$$

We compute the similarity ratio between two contribution types using the filtered PageRanks lists computed in § 3.3.1. The result depicted in Figure 3.3 shows that the degree of similarity remains under 0.5 for the low values of $r$ ($r < 0.2$). For certain pairs of contributions (e.g., the *Argument/Modification* and *Argument/Vote* pairs), the similarity remains stable between 0.3 and 0.4. Consequently, we argue that PageRanks for these types of contributions are correlated. The correlation is weaker for other pairs (e.g., *Modification/Source* and *Modification/Vote*) that seem to be independent. The fact that the *Argument/Source* curve of the graph is the only one that converges towards 1 for the highest values of $r$ is due to the fact that both *Arguments* and *Sources* can only be used as replies to *Proposals* and *Modifications*, i.e., $PR_{Argument}$ and $PR_{Source}$ contain the same users after filtering. We can conclude that the users who receive most of the *Argument* replies tend to also centralize other types of replies, but this centralization does not apply for most other types.

Although contributors tend to weakly centralize contributions of different types in most cases, we want to know if this centralization is stronger around *Proposals*. This leads us to compute the similarity of the *Proposals* receiving the most replies of each type. Let:

* $P_{type_a}$ (resp. $P_{type_b}$) denotes the list of *Proposals* in ascending order depending on the number of replies of $type_a$ (resp. $type_b$)
<figure><img src="image_9.png" /><figcaption>Figure 3.4: Correlation of the amount of replies to a proposal for different contribution types.</figcaption></figure>

* $P_{type_a,r}$ (resp. $P_{type_b,r}$) denotes the sublist of $P_{type_a}$ (resp. $P_{type_b}$) obtained after picking the $r$ ($r$ defined as before) share of the *Proposals* with the most replies.

The similarity ratio $s_{reply}$, which provides the level of similarity between $P_{type_a,r}$ and $P_{type_b,r}$, is defined as:

$$s_{reply} = \frac{|P_{type_a,r} \cap P_{type_b,r}|}{|P_{type_a,r} \cup P_{type_b,r}|}$$

The results of the computation (see Figure 3.4) highlight two main tendencies. First, for the lowest values of $r$ (i.e., $r < 0.2$), the levels of similarity for pairs of reply types follow two tendencies. The *Argument/Modification*, *Argument/Vote* and *Modification/Vote* pairs have a high level of similarity ($s_{reply} > 0.5$) while this correlation is clearly weaker for all the pairs that contain the *Source* type of reply, i.e., the *Argument/Source*, *Modification/Source* and *Source/Vote* pairs. The computation becomes less relevant as $r$ grows since $P_{type_a}$ and $P_{type_b}$ contain the same users, i.e., $s_{reply}$ converges towards 1 for the highest values of $r$. In other words, although the *Proposals* that gather the most replies of a given type tend to also receive the most replies of other types, the correlation does not apply to the *Source* reply type that appears independent from the others. However, we must consider that this lack of apparent correlation may result from the low number of *Source* replies in the dataset (see Figure 3.2) and must not be interpreted as an inverse correlation or a decorrelation.

Second, the comparison of the filtered version of the correlation of PageRanks (see Figure 3.3) with the graph obtained from the computation of $s_{reply}$ evidences that the similarity level grows faster for the latter. This difference in the shape of the curves may be related to a low impact of initial authors on the concentration of replies around a single contribution. Therefore, replies would be sent to a contribution due to its content rather than its author.

Our analysis shows that *Proposals* that receive the most replies of a certain type –excluding *sources*– tend to also centralize replies of the other types. This can lead to situations where a *Proposal* receives, e.g., a great number of *Votes* and very few
<figure><img src="image_10.png" /><figcaption>Figure 3.5: Correlation of activity for each contribution type.</figcaption></figure>

Sources, becoming de facto a subject that both meets a great interest from the contributors and proposes very few material to form an opinion. We derive the following recommendation:

REC. 2. We recommend to give the opportunity to users to add tags to Proposals such as “sources needed” or “lacking arguments” to attract potential contributors who may enrich their least complete parts. As part of our ongoing work, we are also investigating the automated labeling of contributions depending on the types of contributions they lack using a machine learning approach based on, inter alia, the number of replies of different types to a Proposal or their content, as proposed in [81].

## 3.4 How to mitigate the influence of the most active users?

In order to further understand the behavior of contributors, we analyze the correlation between the activity of a user for different types of contribution. This correlation allows us to know whether the most active contributors for a type of contribution are also very active in another type, or the two behaviors are independent. Let:

* $U_{type_a}$ (resp. $U_{type_b}$) be the list of users sorted by ascending order depending on the number of contributions of $type_a$ (resp. $type_b$) they have published;

* $U_{type_a,r}$ (resp. $U_{type_b,r}$) be the sublist of $U_{type_a}$ (resp. $U_{type_b}$) obtained after picking the $r$ ($r$ defined as before) share of users with the highest number of contributions in the list.

The $s_{activity}$ ratio, which provides the level of similarity between $U_{type_a,r}$ and $U_{type_b,r}$, is defined as:

$$s_{activity} = \frac{|U_{type_a,r} \cap U_{type_b,r}|}{|U_{type_a,r} \cup U_{type_b,r}|}$$
Figure 3.5 highlights two different kinds of evolution of the similarity depending on *r*. The first group includes pairs of contribution types for which the level of similarity does not significantly increase while *r* < 0.4, and raises significantly afterwards. This group is composed of all the pairs containing the *Vote* contribution type. Here, the similarity level increases only because the wider both sublists are, the more likely a user is to be found in both of them. Therefore, we cannot declare that there is a correlation between the activity in *Vote* and other types of contributions.

The second group contains the pairs for which the level of similarity is low for
$r < 0.1$ but drastically increases afterwards and finally remains almost equal to 1
after the first half of the graph, resulting in a square root-shaped curve. This group
contains all the pairs containing two text-based contribution types, i.e., any type
except *Vote*. For these pairs of contribution types, even if the 5-10% most active
contributors are completely different, we can see that their top 20% is very similar.
The most visible example of this group is the *Proposal/Source* curve of the graph.
We conclude that, for pairs in this group, there is a correlation between the activity
in both contribution types. This type of behavior is similar to the one identified by
Vasilescu *et al.* [165] in Q&A platform StackOverflow and version control platform
GitHub, with persons producing a high number of commits in GitHub projects
also tending to answer a high number of questions on StackOverflow. However, we
also identify that the most active contributors ($r < 0.05$) in one type tend to be
specialized in this type and do not produce as many contributions of other types.

The analysis further indicates that, for text-based contributions, even though
the 10 to 20% most active contributors for a specific type of contribution tend to
also contribute to other types above the average, there is a small minority of special-
ized contributors who focus on producing contributions of one specific type. This
leads us to infer that the presence of extremely active contributors, whether they are
specialized in one type of contribution or versatile, can be a potential source of bias
in participation. Indeed, these kinds of behavior can lead to situations where, for a
given *Proposal*, most reply contributions of one or more types are submitted by a
specific opinion group. This kind of situations could reduce the capability of contrib-
utors to form an informed opinion on a *Proposal* and bias their final (dis)approval.
Consequently, we introduce the following recommendation:

**REC. 3.** To reduce the risks induced by the centralization of contributions of a given type around a small set of contributors, it is necessary to identify the Proposals whose replies are made by a small set of active contributors. This detection can be achieved using graph-based anomaly detection [4] where: contributors and Proposals are two different types of vertices, and a contributor is linked to a Proposal by an edge if they have submitted a contribution related to the said Proposal. The Proposals that are detected as receiving an abnormally high number of replies from a few contributors are then highlighted to other contributors, so that they can provide new perspectives. This detection could lead to a reduction of the bias induced by the original contributors' perspective.
## 3.5 Does the positioning of *Proposals* have an impact on participation?

In order to better understand which users and which *Proposals* tend to receive the most replies, we hypothesize that the positioning of *Proposals* on the consultation's user interface has an impact on their popularity. Indeed, the *RepNum* platform displays *Proposals* according to the two following criteria:

**Categorization:** *Proposals* are grouped according to the categories of the consultation's structure (see Figure 3.1). As a result, contributors have easier access to the content related to the categories at the top. This potentially creates a bias toward these categories and thus an impact on replies to content in lower categories.

**Pinning:** Within each category, the first *Proposals* displayed are always the ones composing the initial bill project, that is, the ones proposed by the government. These *Proposals* are "pinned" at the top of their category. The other *Proposals* can be sorted according to different criteria (e.g., date of publication, number of *Votes*, etc.) but are sorted randomly by default. This layout implies that the *Proposals* made by the government are more accessible to the contributors. This is a potential source of bias that can lead users to focus on government *Proposals* at the expense of public participation.

To assess the actual impact of this design choice of the *RepNum* platform, we statistically analyze the number of replies of each type to the *Proposals*, according to the two above criteria. In our first analysis, we group *Proposals* according to their categorization (see Figure 3.1). We then compute the quartiles and median for the number of replies of each type to a *Proposal* depending on the three levels (see Table 3.4 in Appendix). Regardless of the chosen categorization level, we notice small variations in the three statistical values. These variations tend to be wider for *Votes* than for other types of replies, with peaks identified in Section II.2.2, and Section III.3.1 of the consultation. However, we cannot identify any clear decrease or increase in the number of replies depending on the positioning of the category in the user interface. Nevertheless, we can see that the way categories are presented has a clear impact on the number of *Proposals* submitted within a section. Indeed, the number of *Proposals* per section tends to decrease as the category is positioned lower in the user interface. This tendency is more significant for coarser levels. Therefore, we consider that even though the impact of the positioning of contributions based on categorization is not significant enough to be identified as a bias generator in the contribution of replies to a *Proposal*, it has an impact on the number of *Proposals* submitted in a section and reduces the participation for the related subjects. We make the following recommendation:

**REC. 4.** *The number of *Proposals* related to the topmost categories –as displayed– being higher than that of the lower categories, we recommend either to sort categories randomly so that each contributor is exposed to different topics of the consultation, or to implement another method to display categories of *Proposals* on a more horizontal layout.*

The second analysis compares the popularity of all the *Proposals* of the consultation with those made by the government. First, we group the *Proposals* made by the
<figure><img src="image_11.png" /><figcaption>Figure 3.6: Steps for building the Online Public Consultation KG.</figcaption></figure>

government according to their part, chapter and section (see Table 3.4 in Appendix). The number of *Proposals* from the government being particularly low (between 1 and 6 per section), we only compute the median for the number of replies of each type. Then, we compare these values to the associated medians and quartiles for the whole set of *Proposals* computed previously. The comparison shows that for every type of reply and category level, the median of replies to government *Proposals* of a category is always higher or equal to the third quartile of the number of replies to *Proposals* in this category. We conclude that pinning *Proposals* has a significant impact on the number of replies received, and consequently is a bias generator and centralizes the participation around the *Proposals* made by the government. This leads to the following recommendation:

**REC. 5.** As the pinning of *Proposals* tends to centralize the replies around a small set of pinned *Proposals*, we recommend either not to implement this option or to change the selection criteria to prioritize pinning *Proposals* that would benefit the most from a high number of replies, such as those that are highly controversial.

## 3.6 Building an Online Public Consultation Knowledge Graph

The three previous sections of this chapter allows us to identify biases in the design of the *RepNum* platform and to recommend solutions to avoid reproducing such issues in similar online consultations. In this section, we propose to improve information retrieval in a consultation platform using a knowledge graph. Figure 3.6 shows the different stages of our methodology for building the consultation KG. We detail these stages in the rest of this section by building a KG for the *République Numérique* consultation.

### 3.6.1 Topic modeling

We use Latent Dirichlet Allocation (LDA) [79] on the proposals submitted to the *RepNum* consultation to identify the different topics. Each proposal is represented as a document, and LDA is an algorithm for topic modeling in a set of documents
<figure><img src="image_12.png" /><figcaption>Figure 3.7: LDA representation of the main topics discussed in the *République Numérique* citizen consultation and their most frequent words. Topic labels were chosen a posteriori by the authors of this work to help differentiate them.</figcaption></figure>

where each topic is associated with the different N-grams<fnref n="2" /> based on their number of occurrences in the different documents. Each proposal is then associated with all relevant topics based on a truth value score. Using this algorithm, we need to choose the number *k* of topics to identify. We applied LDA for *k* ∈ [4, 15] and assessed the suitability of each version of the topic modeling based on the consistency of the 20 most frequent words for each topic. We consider *k* = 5 to be the parameter giving the most coherent results for the *République Numérique* citizen consultation. Figure 3.7 shows the five main topics identified and their most frequent words. We use these topics, in addition to the proposal's title, author, and content, to further characterize the different proposals.

3.6.2 Entity Recognition and Linking

We use a French-language adaptation of DBpedia Spotlight [49, 108] (DBS for short in the following) to identify entities in our proposals. DBS extracts important words from a given text and links them to resources in DBpedia [18]. We have set the “confidence” parameter to 0.6 and left everything else to default. Our observations about DBS concern the French model. Linking the most important elements in the proposals to DBpedia resources allows us to associate these elements with definitions from Wikipedia and connect proposals based on the resources they mention. To eliminate certain common false associations identified in preliminary tests, we filter the obtained results:

* DBS tends to identify years and dates as important elements of the proposals and link them with the aggregation Wikipedia page for events happening at this given date - e.g., https://en.wikipedia.org/wiki/December_13-. In

<footnote n="2">²A sequence of 1 or more words usually written consecutively. For example, both “text” and “knowledge graph” are N-grams in the context of this section.</footnote>
the context of an online consultation, such elements are often mentioned in contexts where this linking is not relevant. Therefore, we exclude these resources before performing Named Entity Linking (NEL).

* We also observe that the model tends to identify certain N-grams as film titles. This has almost only led to incorrect identification of common N-grams as film titles in the context of the *RepNum* consultation. Consequently, we have filtered non-documentary films out of the resource graph with which we associate our entities. The choice to keep documentary films is made to give citizens the possibility to add references to support their proposals.

Then, we identified an additional issue: acronyms with multiple definitions are often associated with the wrong resource. For example, the acronym “CADA” is used in a proposal to mention the “Administrative Documents Access Commission” (in French, *Commission d’Accès aux Documents Administratifs*). However, this entity is linked to “Shelter for Asylum Seekers” (in French, *Centre d’Accueil de Demandeurs d’Asile*), even though “administrative documents” is mentioned further in the proposal. This issue has been confirmed with similar results by using DBS on another online consultation called RUA (Open data available at https://www.data.gouv.fr/fr/datasets/consultation-vers-un-revenu-universel-dactivite-1/). We solve this issue by applying the following process to each acronym identified as important in a proposal.

* If DBpedia has a node with a name identical to the acronym:

- If this node is a specific resource (e.g., a Wikipedia page) or redirects to it, we associate the acronym with this resource.

- Else, if the node is a disambiguation node for multiple resources, we gather the textual abstract attached to each resource. Then, using the SBERT [137, 139] model for Semantic Textual Similarity mining, we apply cosine similarity to each abstract with the proposal containing the acronym. Finally, we associate the acronym with the resource whose abstract has the highest cosine similarity with the proposal.

* Else, using the Wikipedia API, we search for the acronym and gather the 5 best results. Then, similarly to the previous dash, we use SBERT's cosine similarity to identify the Wikipedia page whose abstract is the most similar to the proposal. Finally, we link the acronym with the DBpedia resource associated with the identified Wikipedia page.

Performing Named Entity Recognition (NER) and NEL as mentioned earlier generates a set of proposals where certain N-grams are associated with a DBpedia resource. The obtained results are presented in Table 3.3. A considerable proportion of the proposals (16.2 %) are not associated with any entity, likely because these proposals are shorter than others. Specifically, while the median number of tokens<fnref n="3" /> for proposals with zero annotations is 80, it is 188 for proposals with one or more annotations. Moreover, more than one-third of the proposals (36.6 %) are associated

<footnote n="3">³In NLP vocabulary, a token refers to a part of a string. In this study, a token is either a number, a word, or a punctuation mark.</footnote>
<table><caption>Table 3.3: NER and NEL results using DBpedia Spotlight on proposals of the RepNum consultation.</caption><thead><tr><td>Metric</td><td>Value</td></tr></thead><tbody><tr><td>Number of proposals</td><td>692</td></tr><tr><td>Raw number of entities</td><td>3,463</td></tr><tr><td>Number of different entities</td><td>1,015</td></tr><tr><td>Number of entities linked with exactly 1 proposal</td><td>680</td></tr><tr><td>Number of entities linked with 5 or more proposals</td><td>82</td></tr><tr><td>Number of annotations for entities linked with 5 or more proposals</td><td>1,655</td></tr><tr><td>Number of proposals without any annotation</td><td>112</td></tr><tr><td>Number of proposals with 5 or more annotations</td><td>253</td></tr><tr><td>Median number of annotations per proposal</td><td>3</td></tr></tbody></table>

with at least five entities. Also, the number of entities linked to at least one proposal is greater than the number of proposals. However, although the number of entities connected to five or more proposals is significantly lower, almost half (47.8 %) of the annotations link proposals to these entities.

### 3.6.3 Resulting Knowledge Graph

Figure 3.8 shows a visual representation of our KG. We store the generated KG using the JSON-LD format [157]. As JSON-LD is an RDF serialization, querying the graph using SPARQL is possible.

The graph we propose facilitates the issuance of new queries in consultations by both citizens and platform administrators. For instance, one can retrieve all proposals mentioning a specific entity while focusing on a particular topic, such as “all the proposals mentioning the National Commission on Informatics and Liberty (CNIL)” with an LDA-score greater than 0.6 for the “Data Protection” topic. Furthermore, the model allows for summarizing contributions by identifying the topics and entities mentioned in a consultation or linking entities to specific groups of users. By using this method, platform administrators can enhance the transparency of their reports on citizen consultations by incorporating graph-based data alongside responses to a diverse array of proposals<fnref n="4" />, selected either randomly or based on predetermined criteria. Lastly, our work can be generalized to most online participatory platforms to connect contributions from various online consultations.

## 3.7 Threats to validity

For the analysis of the centralization of contributions around contributors and *Proposals* (§ 3.3.2), we did not take into account the relevance of contributions. Indeed, certain contributions can be out of the scope of the consultation, mischievous, or duplicates of pre-existing *Proposals*. This factor could be meaningful to explain why a *Proposal* receives many replies or not, as the least relevant *Proposals* are likely to

<footnote n="4">⁴For instance, see https://www.republique-numerique.fr/project/projet-de-loi-numerique/step/reponses</footnote>
<figure><img src="image_13.png" /><figcaption>Figure 3.8: Visualization of a generic consultation stored in JSON-LD.</figcaption></figure>

be ignored by contributors. This should be considered to balance the contributions received by *Proposals*, so as to not highlight irrelevant *Proposals* that receive little attention.

We focus this study on a specific consultation. It is therefore subject to a demographic bias concerning the studied population and our findings could differ in other consultations. We mitigate this risk by choosing the largest dataset we have found, concerning the number of both contributors and contributions. However, the dataset still contains very few entries for certain types of contributions such as *Sources*, which reduces the representativeness of our sample for these types of contributions. Therefore, reproducing similar work on different datasets is worth investigating to generalize our recommendation to other participatory platforms.

When we evaluated the impact of the positioning of *Proposals* on participation (§ 3.5), our conclusions and recommendations were based on the default positioning of *Proposals* on the user interface. However, we were not able to get statistics about the percentage of contributors using this default interface. Still, lacking this information has no impact on the validity of REC. 4 since, regardless of the modifications of the interface proposed by the platform, categories are always displayed in the same order. Nevertheless, if most users did choose a non-default positioning of *Proposals* which would not put pinned *Proposals* forward, then the centralization of contributions around the said pinned *Proposals* would be only caused by the fact that they have been issued by the government. In that case, REC. 5 would not effectively solve the identified problem since it would not be induced directly by the user interface.

## 3.8 Related Work

Ben Jabeur et al. [24] introduce three new algorithms based on PageRank to characterize the specific behavior of three different groups (*influencers*, *leaders* and *discussers*) of key users in Twitter. Weng et al. [174] present another PageRank-based algorithm, also targeting Twitter but focused on the detection of influencers depending on the topic. Our work distinguishes from the above studies by addressing participatory platforms. However, their analysis identifies different types of following behaviors depending on the topical similarity between users, in a way similar to ours. This work suggests that the dedicated customization of PageRank would
allow the detection of influential contributors according to the given use of online citizen participation platforms, which is area for our future work.

Conover et al. [46] propose to use label propagation [136] to identify clusters of users belonging to the same opinion group. Their approach allows to identify the patterns of interaction between users from the two clusters they identify –left- and right-leaning users– for the different types of interactions proposed by the platform. This method can be relevant in our case, especially as a mean to improve the reliability of REC. 3 in the detection of opinion groups centralizing the discussion around a specific proposition.

Khan et al. [85] present a requirement engineering approach based on the extraction and evaluation of arguments from end-users feedbacks. They apply an argumentation theory and machine learning-based approach to establish requirements for a Google Maps feature. Our approach differs greatly from theirs since we draw our recommendations based on users’ behaviors and on the flaws they highlight on the platform we study. We argue that our approach has the advantage of directly relying on users’ interactions with the platform rather than on users feedback. Therefore, our approach allows the identification of issues even if users are not aware of their existence. However, argument mining-based approach has the advantage of directly giving voice to platform users and therefore could be used to improve online participatory platforms based on arguments provided by citizens.

While the focus of this chapter has been on informing the design and/or enhancement of participatory platforms based on the analyses of a past consultation, related studies are concerned with questioning the overall development process of these platforms. For instance, Knutas et al. [86] present a case study of the software development processes that emerged from the “*Code for Ireland*” initiative. They provide a description of the implemented practices together with their achievements. They also identify specific challenges in the development process, notably concerning the specification of requirements or the engagement of stakeholders, and propose a set of recommendations to solve the problems identified.

Finally, the study of the *RepNum* consultation has received much attention from the French-speaking social science community. This includes the work of Laurent et al. [91] who analyze the graphic design of the *RepNum* user interface and the interactions it enables with the platform. They highlight the inadequacy of the contribution means offered by the platform with respect to the diversity of the points of view expressed. They also investigate the use of the platform in the context of the French representative democracy. Alexis et al. [5] also analyze the functionality of the *RepNum* platform, taking into account the tools offered to the user for the consultation and investigating the types of discourse induced. They highlight a depoliticization of the discourse through the neutralization<fnref n="5" /> of the subject. These studies are complementary to ours and discuss other keys to understand how the *RepNum* consultation worked.

<footnote n="5">⁵Erasure of oppositions between different points of view and the production of signs of the evidence of a single point of view, assumed transparent (translated from [122]).</footnote>
## 3.9 Conclusion

We analyzed the different flaws of the *RepNum* platform regarding citizen participation. Our findings show a high degree of centralization of contributions around a small set of *Proposals*. This centralization is more related to the content of *Proposals* themselves than to the identity of their authors. We also highlight the existence of extremely active contributors, with some of them being specialized in a specific type of contribution and others being more versatile. Finally, we identify the impact of the positioning of *Proposals* on the number of related contributions submitted. These different situations constitute threats to participation, and therefore to the inclusion of citizens in public decision making. To solve the problems arising from them, we formulate five recommendations oriented towards the design of participatory platforms.

We conclude that the design of participatory platforms should be considered as a vector of bias to the participation itself, and the behavior of contributors is a meaningful indicator to identify such issues. However, to the best of our knowledge, contemporary participatory platforms do not implement certain modern software features, which could be used to improve their existing features, involve citizens deeply in the process and respond to new use cases. We propose implementing a tool to improve exploration of a set of proposals using an automatically constructed KG, to facilitate both the identification of relevant proposals for a citizen and the summarization of contribution by platforms administrators. Moreover, modern CSCW tools such as collaborative editing would help participants elaborate collective proposals. Social recommendation [60] could also be leveraged to highlight relevant topics for contributors or help them find co-writers for common proposals.

The next chapter of this thesis will present the second contribution of my Ph.D.: an interview-based study presenting a description framework for online citizen participation based on the objectives pursued by platform maintainers and the technical features implemented.

# Appendix: Data tables for Section 3.5 (Also available on the Git repository)
<table><caption>Table 3.4: Amount of replies to contributions of all types for each category of the consultation.</caption><thead><tr><th rowspan="2">Part</th><th rowspan="2">Chapter</th><th rowspan="2">Section</th><th rowspan="2">Nb. of Proposals</th><th colspan="3">Arguments</th><th colspan="3">Modifications</th><th colspan="3">Sources</th><th colspan="3">Votes</th></tr><tr><th>Q1</th><th>Med</th><th>Q3</th><th>Q1</th><th>Med</th><th>Q3</th><th>Q1</th><th>Med</th><th>Q3</th><th>Q1</th><th>Med</th><th>Q3</th></tr></thead><tbody><tr><td rowspan="10">Part I</td><td rowspan="3">Chapter 1</td><td>Section 1</td><td>160</td><td>1</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>14</td><td>24</td><td>81</td></tr><tr><td>Section 2</td><td>47</td><td>1</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>7</td><td>23</td><td>48</td></tr><tr><td>Section 3</td><td>33</td><td>1</td><td>3</td><td>6</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>9</td><td>23</td><td>75</td></tr><tr><td rowspan="3">Chapter 2</td><td>Whole chapter</td><td>240</td><td>1</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>11</td><td>23</td><td>76</td></tr><tr><td>Section 1</td><td>59</td><td>0</td><td>2</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10</td><td>24</td><td>52</td></tr><tr><td>Section 2</td><td>25</td><td>1</td><td>2</td><td>6</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>8</td><td>26</td><td>203</td></tr><tr><td rowspan="2">Whole part</td><td>Whole chapter</td><td>84</td><td>0</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>10</td><td>24</td><td>81</td></tr><tr><td>Whole part</td><td>324</td><td>1</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>10</td><td>24</td><td>77</td></tr><tr><td rowspan="10">Part II</td><td rowspan="3">Chapter 1</td><td>Section 1</td><td>30</td><td>1</td><td>4</td><td>6</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>11</td><td>24</td><td>62</td></tr><tr><td>Section 2</td><td>19</td><td>0</td><td>1</td><td>2</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>4</td><td>14</td><td>21</td></tr><tr><td>Section 3</td><td>23</td><td>1</td><td>2</td><td>6</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8</td><td>15</td><td>29</td></tr><tr><td rowspan="3">Chapter 2</td><td>Section 4</td><td>31</td><td>0</td><td>1</td><td>4</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8</td><td>12</td><td>39</td></tr><tr><td>Whole chapter</td><td>103</td><td>1</td><td>2</td><td>6</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>8</td><td>16</td><td>48</td></tr><tr><td>Section 1</td><td>82</td><td>0</td><td>1</td><td>3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>4</td><td>9</td><td>27</td></tr><tr><td rowspan="3">Chapter 3</td><td>Section 2</td><td>19</td><td>0</td><td>2</td><td>10</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>8</td><td>42</td><td>248</td></tr><tr><td>Whole chapter</td><td>101</td><td>0</td><td>1</td><td>4</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>4</td><td>11</td><td>43</td></tr><tr><td>Whole part</td><td>204</td><td>0</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>6</td><td>13</td><td>48</td></tr><tr><td rowspan="10">Part III</td><td rowspan="3">Chapter 1</td><td>Section 1</td><td>60</td><td>0</td><td>1</td><td>3</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>30</td></tr><tr><td>Section 2</td><td>30</td><td>1</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>4</td><td>10</td><td>28</td></tr><tr><td>Whole chapter</td><td>90</td><td>0</td><td>1</td><td>4</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>4</td><td>9</td><td>28</td></tr><tr><td rowspan="3">Chapter 2</td><td>Section 1</td><td>19</td><td>1</td><td>3</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>5</td><td>13</td><td>37</td></tr><tr><td>Section 2</td><td>12</td><td>0</td><td>3</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>4</td><td>25</td><td>83</td></tr><tr><td>Whole chapter</td><td>31</td><td>1</td><td>3</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>5</td><td>16</td><td>83</td></tr><tr><td rowspan="3">Chapter 3</td><td>Section 1</td><td>13</td><td>2</td><td>3</td><td>6</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>23</td><td>114</td><td>396</td></tr><tr><td>Section 2</td><td>14</td><td>0</td><td>3</td><td>4</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>3</td><td>13</td><td>76</td></tr><tr><td>Whole chapter</td><td>16</td><td>1</td><td>4</td><td>10</td><td>0</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>12</td><td>52</td><td>174</td></tr><tr><td rowspan="2">Whole part</td><td>Whole chapter</td><td>43</td><td>1</td><td>4</td><td>6</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>12</td><td>51</td><td>185</td></tr><tr><td>Whole part</td><td>164</td><td>1</td><td>2</td><td>5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>4</td><td>13</td><td>70</td></tr></tbody></table>

<table><caption>Table 3.5: Amount of replies to governmental contributions of all types for each category of the consultation.</caption><thead><tr><th>Part</th><th>Chapter</th><th>Section</th><th>Nb. Proposals</th><th>Med. Arguments</th><th>Med. Modifications</th><th>Med. Sources</th><th>Med. Votes</th></tr></thead><tbody><tr><td rowspan="10">Part I</td><td rowspan="3">Chapter 1</td><td>Section 1</td><td>3</td><td>44</td><td>32</td><td>3</td><td>1319</td></tr><tr><td>Section 2</td><td>1</td><td>38</td><td>30</td><td>3</td><td>892</td></tr><tr><td>Section 3</td><td>3</td><td>23</td><td>19</td><td>1</td><td>777</td></tr><tr><td rowspan="3">Chapter 2</td><td>Whole chapter</td><td>7</td><td>38</td><td>27</td><td>1</td><td>892</td></tr><tr><td>Section 1</td><td>1</td><td>25</td><td>49</td><td>2</td><td>822</td></tr><tr><td>Section 2</td><td>2</td><td>40</td><td>12</td><td>1</td><td>706</td></tr><tr><td rowspan="2">Whole part</td><td>Whole chapter</td><td>3</td><td>40</td><td>49</td><td>2</td><td>822</td></tr><tr><td>Whole part</td><td>10</td><td>38</td><td>27</td><td>1</td><td>822</td></tr><tr><td rowspan="10">Part II</td><td rowspan="3">Chapter 1</td><td>Section 1</td><td>1</td><td>40</td><td>24</td><td>1</td><td>1426</td></tr><tr><td>Section 2</td><td>1</td><td>47</td><td>38</td><td>0</td><td>796</td></tr><tr><td>Section 3</td><td>2</td><td>24</td><td>16</td><td>1</td><td>379</td></tr><tr><td rowspan="3">Chapter 2</td><td>Section 4</td><td>1</td><td>31</td><td>24</td><td>2</td><td>488</td></tr><tr><td>Whole chapter</td><td>5</td><td>38</td><td>24</td><td>1</td><td>669</td></tr><tr><td>Section 1</td><td>6</td><td>23</td><td>16</td><td>0</td><td>495</td></tr><tr><td rowspan="3">Chapter 3</td><td>Section 2</td><td>1</td><td>35</td><td>26</td><td>1</td><td>853</td></tr><tr><td>Whole chapter</td><td>7</td><td>25</td><td>26</td><td>1</td><td>561</td></tr><tr><td>Whole part</td><td>12</td><td>28</td><td>24</td><td>1</td><td>561</td></tr><tr><td rowspan="10">Part III</td><td rowspan="3">Chapter 1</td><td>Section 1</td><td>1</td><td>22</td><td>41</td><td>2</td><td>368</td></tr><tr><td>Section 2</td><td>2</td><td>9</td><td>7</td><td>5</td><td>226</td></tr><tr><td>Whole chapter</td><td>3</td><td>13</td><td>7</td><td>5</td><td>368</td></tr><tr><td rowspan="3">Chapter 2</td><td>Section 1</td><td>1</td><td>21</td><td>10</td><td>5</td><td>392</td></tr><tr><td>Section 2</td><td>1</td><td>81</td><td>30</td><td>6</td><td>1380</td></tr><tr><td>Whole chapter</td><td>2</td><td>21</td><td>10</td><td>5</td><td>392</td></tr><tr><td rowspan="3">Chapter 3</td><td>Section 1</td><td>1</td><td>19</td><td>24</td><td>7</td><td>498</td></tr><tr><td>Section 2</td><td>1</td><td>45</td><td>22</td><td>5</td><td>408</td></tr><tr><td>Section 3</td><td>1</td><td>24</td><td>11</td><td>5</td><td>833</td></tr><tr><td rowspan="2">Whole part</td><td>Whole chapter</td><td>3</td><td>24</td><td>22</td><td>5</td><td>498</td></tr><tr><td>Whole part</td><td>8</td><td>21</td><td>11</td><td>5</td><td>392</td></tr></tbody></table>
# Chapter 4

## Objectives and features of contemporary platforms

### Contents

<table><tr><td>4.1</td><td>Introduction</td><td>69</td></tr><tr><td>4.2</td><td>Methodology</td><td>71</td></tr><tr><td>4.3</td><td>Different objectives of citizen participation platforms</td><td>73</td></tr><tr><td>4.3.1</td><td>Surveying citizens' opinions</td><td>73</td></tr><tr><td>4.3.2</td><td>Dialogue between citizens and an institution</td><td>74</td></tr><tr><td>4.3.3</td><td>Creation of opinion groups or neighborhood councils</td><td>75</td></tr><tr><td>4.3.4</td><td>Co-construction of projects</td><td>76</td></tr><tr><td>4.4</td><td>Different types of platforms for contemporary online citizen participation</td><td>77</td></tr><tr><td>4.4.1</td><td>"Suggestion box"-like platforms</td><td>77</td></tr><tr><td>4.4.2</td><td>Interactive platforms</td><td>79</td></tr><tr><td>4.5</td><td>How to enrich online participatory platforms</td><td>80</td></tr><tr><td>4.5.1</td><td>Collaborative systems</td><td>80</td></tr><tr><td>4.5.2</td><td>Natural Language Processing for large-scale processing of contributions</td><td>81</td></tr><tr><td>4.6</td><td>Threats to validity</td><td>83</td></tr><tr><td>4.7</td><td>Conclusion</td><td>83</td></tr></table>

This chapter is based on our paper "Citizen participation platforms' objectives and how to reach them: an interview-based analysis", written with Rafael Angarita and Valérie Issarny. At the time of submission of my Ph.D. thesis, this paper has not been published yet.

### 4.1 Introduction

The use of online platforms supporting citizen participation has become more common in the recent years, allowing the organization of citizen participation at larger
scales [12, 91], both concerning their number of contributors and the geographical
distance between them. Such platforms implement a wide range of features to help
citizens contribute to a participatory project. These features implement the rules of
a given participatory process as defined by the organizing institution. They also de-
fine the degree of possible interaction between the contributors of a given platform,
allowing, for example, the creation of altered versions of a pre-existing contribution.
However, Ianniello et al. [75] identify that bad quality participatory process design
or citizen collaboration has been a major obstacle to successful citizen participation
processes in previous contexts. Chapter 3 also identified flaws in the design of a
specific online consultation and its human-computer interface, leading to measur-
able biases in the contributions produced by citizens. Therefore, we consider that,
in the context of online participatory platforms, the features implemented are a key
factor to the success of citizen participation initiatives. Moreover, the high number
of contributions have also been identified as the origin of an information overload
experienced by citizens [38, 132]. Arana-Catania et al. [13] propose implement-
ing machine learning-based features to reduce this information overload and help
contributors identify relevant contributions.

Apart from the features they implement,
participatory platforms also differ depending
on their objectives. One way to characterize
the objectives of a participatory process can
be to use Arnstein's ladder of citizen par-
ticipation [15]. The ladder introduces eight
levels, with each corresponding to a different
level of citizen empowerment, from manip-
ulation to citizen control (see Figure 4.1).
Each level implies different forms of inter-
action between citizens and the concerned
organization as well as among the citizens
themselves. However, even though the level
of citizen empowerment is crucial to differen-
tiate participatory platforms, the objectives
of citizen participation initiatives can also
be defined depending on the type of output
expected by the institution. This differen-
tiation overlaps with the one proposed by
Arnstein's ladder since the higher degrees of
the ladder – called “degrees of citizen power”
– cannot be achieved if the expected input
is simple and individual (e.g., answering a

survey). Moreover, while platform administrators are not always aware of the speci-
ficities of each degree of participation, they clearly identify the type of output ex-
pected in the platforms they maintain and therefore can be more reliably trusted
to describe outputs produced by contributors than the positioning of their platform
on Arnstein's ladder. Consequently, we consider that the type of output expected
is a more suitable criterion to differentiate the objectives of participatory platforms
than their degree of citizen participation.

Using the two aforementioned axes to differentiate participatory platforms –

<figure><img src="image_14.png" /><figcaption>Figure 4.1: Arnstein’s ladder of citi-
zen participation [15]</figcaption></figure>
objectives pursued and features implemented –, this chapter answers the following research questions:

**RQ1:** What are the different objectives set by citizen participation platforms maintainers<fnref n="1" />?

**RQ2:** How do the features implemented in participatory platforms impact the design of citizen participation initiatives?

To answer these questions, we rely on a series of five semi-directed interviews with citizen participation platforms administrators and designers. These interviews allow us to differentiate platforms depending on their objectives (§ 4.3) and on the means they implement to accomplish these objectives (§ 4.4). To ensure the representativeness of our panel of interviewees, we chose to interview stakeholders with diverse professional profiles, from cities of different sizes and who use different software for their participatory platforms (§ 4.2). Based on the different objectives and features identified from our interviews, we formulate recommendations to improve the experience of contributors and administrators of citizen participation platforms (§ 4.5), as well as to guide platform administrators and designers on the development of future participatory platforms. These recommendations are focused on machine learning-based identification of relevant contributions, collaborative editing of contributions, and the integration of pre-existing features that have yet to be implemented within the context of citizen participation platforms.

## 4.2 Methodology

To better understand users' behaviors in online citizen participation platforms and the specific rules of the aforementioned platforms, we conducted a series of five semi-directed interviews with citizen participation platform administrators and providers (profiles of the interviewees are listed in Table 4.2). The choice of using interviews rather than empirical data produced by contributors of citizen participation platforms is motivated by our need to identify the objectives pursued by the organizers of participatory processes (e.g., cities, and public institutions). We consider that such information cannot be obtained without directly interviewing stakeholders working for the aforementioned organizing bodies. The questions we asked were related to the objectives they seek to achieve, the functionalities implemented and the means used to gather and synthesize contributions (see Table 4.1). These interviews allowed us to identify different use cases for citizen participation as well as the software tools implemented in contemporary platforms.

We consider that our panel of interviewees quite accurately represents the diversity of citizen participation platforms. Indeed, our interviewees work for city of various sizes with two under 100k inhabitants, one over 200k and one over three million, in different geographic locations and at different degrees of technical implication. The platforms our interviewees work for have different purposes, including

<footnote n="1">¹In this chapter, we differentiate platforms administrators and maintainers. We use “administrators” to reference the persons who manage participatory platforms and summarize contributions produced. The word “maintainer” is used to reference the entity which creates and maintains a participatory initiative taking place in an online platform (e.g., an institution).</footnote>
<table><caption>Table 4.1: Questions for the interviews. The questions were modified in the case of Interviewee #4 as their company did create and manage multiple participatory platforms.</caption><thead><tr><th>#</th><th>QUESTION</th></tr></thead><tbody><tr><td>#1</td><td>What is the main objective which motivated the creation of your participatory platform?</td></tr><tr><td>#2</td><td>What functionalities have been implemented in your participatory platform?</td></tr><tr><td>#3</td><td>What objective should the different functionalities implemented fulfill?</td></tr><tr><td>#4</td><td>What service of your institution is assigned to the management of your platform? Do you collaborate with an external company?</td></tr><tr><td>#4'</td><td>If you work with an external company, what kind of specifications did you provide? Technical or functional?</td></tr><tr><td>#5</td><td>How do you manage the platform and gather the contributions received?</td></tr></tbody></table>

<table><caption>Table 4.2: Profiles of interviewees. Population of their respective cities were reported in September 2022.</caption><thead><tr><th>INTERVIEWEE #</th><th>PROFILE</th><th>CITY</th><th>POPULATION</th><th>PLATFORM</th></tr></thead><tbody><tr><td>#1</td><td>Platform administrator and designer</td><td>Strasbourg</td><td>287 k</td><td>Created in-house</td></tr><tr><td>#2</td><td>Platform administrator</td><td>Antibes</td><td>73 k</td><td>Decidim</td></tr><tr><td>#3</td><td>Platform administrator</td><td>Issy-les-Moulineaux</td><td>68 k</td><td>Cap Collectif</td></tr><tr><td>#4</td><td>Platform-making company executive</td><td>N/A</td><td>N/A</td><td>Cap Collectif</td></tr><tr><td>#5</td><td>Technical Advisor</td><td>Madrid</td><td>3.3 M</td><td>CONSUL</td></tr></tbody></table>

the collective elaboration of complex projects for the development of a city, the publication of suggestions to improve the quality of life in a neighborhood, or the creation of citizen initiatives to work collectively on a given topic. The means of contribution offered to citizens by the platforms managed by our interviewees represent a large array of possibilities, from the sending of ideas to the organizing institution to the collective elaboration of projects. Moreover, the platforms of our panel are developed both using software explicitly created for the occasion and three different all-purpose participatory systems – namely Decidim, Cap Collectif and CONSUL.

Finally, our interviewees have different types of involvement in their respective participatory platforms. Interviewee #1 is the lead designer of the platform developed for their city, their role is to propose and develop new features for the platform based on the specifications proposed with citizens. Interviewees #2 and #3 are administrators of their respective platforms, they monitor the activity on the platforms, summarize the contributions produced by citizens and interact with neighborhood councils to develop the ideas proposed. Interviewee #4 is an executive for a company designing participatory platforms and therefore has a broader view on the different objectives of their customers. Their company has created participatory platforms for cities of various sizes, colleges, companies or national consultations. Interviewee #5 is a technical advisor for citizen participation in their municipality, and therefore is involved in the technical evolution of their city's participatory platform.

All interviewees were located in France or Spain and were remotely interviewed by video conferencing. Video recordings were collected with interviewees' consent and then transcribed by Rafael Angarita and myself. Quotes from the interviews in the following sections have been translated by Rafael Angarita and myself.

Moreover, we have also studied the functioning of different consultations that used these software platforms and where text-based contributions were possible (e.g., https://www.consultation-rua.gouv.fr/, https://www.decidim.barcelona/),
<table><caption>Table 4.3: Categorization of platforms maintained by interviewees according to their objectives</caption><thead><tr><td>INTERVIEWEE #</td><td>PLATFORM OBJECTIVES</td></tr></thead><tbody><tr><td>#1</td><td>Multiple use-cases in the same platform. All the objectives identified are implemented in at least one use case.</td></tr><tr><td>#2</td><td>Dialogue between the citizen and an institution and creation of geographical communities based on the neighborhood councils system.</td></tr><tr><td>#3</td><td>Surveying citizens' opinions and dialogue between the citizen and an institution. In certain cases, proposals are then transferred to neighborhood councils.</td></tr><tr><td>#4</td><td>Multiple platforms pursuing all the objectives identified.</td></tr><tr><td>#5</td><td>Surveying citizens' opinions, dialogue between the citizen and the municipality and co-construction of projects.</td></tr></tbody></table>

as well as the resulting contributions obtained. We chose these consultations for being those with the highest number of contributions for their respective software platforms while still having their platform or open data available online at the time when we made our selection (September 2021). Certain examples cited in the rest of the chapter have been taken from these consultations but they are considered as background knowledge for this chapter. While none of these platforms contradict the conclusions of this chapter, we primarily base our work on the series of interviews mentioned earlier.

## 4.3 Different objectives of citizen participation platforms

One of the key questions in our interviews was about the objectives of citizen participation platforms (Question 1, Table 4.1). We identify four categories of objectives for platform maintainers: survey opinions, dialogue with the citizens, replicate offline participation groups and co-construct projects. These objectives are non-exclusive and all the platforms of our panel fit into at least two of these categories. We have compiled a list of the platforms that our interviewees work on and categorized them in Table 4.3.

### 4.3.1 Surveying citizens' opinions

Certain platforms of our panel are built to survey the opinions of citizens on several topics. Here, we limit this objective to the case where contributions of multiple contributors are gathered and summarized by the institution as a source of data concerning the general opinion of citizens, without the ambition to answer to contributions individually. Generally, these platforms implement a workflow where citizens answer a series of multiple-choice questions or short open-ended questions (e.g., “How would you beautify your neighborhood?”<fnref n="2" />). Interviewee #4 describes these platforms as follows:

> [They are] opinion polls or surveys where you want the opinions of people, where you potentially want to put them in relation to their situation, where you can agglomerate data and analyze them. However, you do not

<footnote n="2">²Question found in the https://idees.paris.fr platform.</footnote>
have any ambition concerning user interactions, like making everyone participate in the same space, take one another's opinions into account and interact together.

Here, the platform maintainers delegate to citizens either the ideation process, as described by [128], or the assessment of ideas already expressed in another context (e.g., offline neighborhood council, department of the city hall). Therefore, administrators of this type of platform generally argue that collaboration among citizens is unnecessary in these specific situations. Interviewee #1, who is the administrator of a platform where multiple participatory goals coexist, explains that they “try to differentiate the information depending on the degree of implication of the citizens and of feedback to them”. Use cases where the objective is to survey citizens’ opinions are therefore annotated as the ones where contributors will receive very little individual feedback and will have very limited involvement in related projects. We identify this objective as closely tied to Arnstein’s [15] definition of the “consultation” degree of participation, “inviting citizens’ opinions”, with the already identified issue that “it offers no assurance that citizen concerns and ideas will be taken into account”.

### 4.3.2 Dialogue between citizens and an institution

An objective often mentioned by participatory platforms administrators we have interviewed is the capability to create a dialogue between citizens individually and an institution. Indeed, to a certain extent, all the platforms in our panel seek to facilitate such dialogue. Interviewee #2 even cites this objective early in our interview as their main motivation to create an online participatory platform in addition to their already existing offline neighborhood councils, since the latter “do not give the opportunity to everyone to express themself and to stay aware of everything happening in the city”. We break this objective down into two sub-objectives: the capability for the institution to share information with the citizens – which is close to the “information” degree of Arnstein’s ladder [15]” – and the possibility for the citizens to provide feedback to the institution on specific topics – similarly to the “consultation” degree –, generally implemented using a two-way vertical workflow with little to no interaction among citizens. Moreover, our interviewees often quote this objective when talking about the signaling of simple issues within the city (e.g., garbage on the sidewalk, out-of-order traffic lights, etc.) or in the case of petition hosting systems, where the range of interactions between citizens tends to be very limited. Nevertheless, given a scope where a single citizen is not enough to provide complete feedback, collaboration among contributors can be useful to ponderate the opinions of citizens depending on their situation. For example, Interviewee #2 mentions the case of a problem with the road system which happened before they deployed their participatory platform:

> Some people were telling us about a speed problem on a street and were asking us to make it a one-way lane. The mayor observed that people in neighborhood councils may not have the same concerns as the residents of the street, even though they live in the same neighborhood. [...] We gathered the opinions of the residents of this street by putting a letter
in their mailbox, and we realized that they did not fully agree with the proposals made by the neighborhood council.

In that case, the lack of a mechanism for citizens to review and engage with one another's proposals could have led to a situation where a decision is taken without the opinion of the most impacted residents. Even though this potential issue was avoided by the active intervention of the institution, we argue that allowing citizens to express their point of view in an online participatory platform would have also been a reliable mean to receive more diverse feedbacks in addition to those expressed by the members of the neighborhood councils.

This objective of dialogue is often pursued on a wider scope than only participatory platforms, generally including interaction between the citizens and institutions on social media. In the case of the platform maintained by Interviewee #3, their objective is to integrate the feedback received through social media to their “Citizen Relationship Management” system. This choice of multiplying the interaction channels, rather than centralizing all the means of information and feedback in the participatory platform, is motivated by their observation that “people tend to use any medium to communicate with the city”. We notice that this diversity of channels is only mentioned by our interviewees when they talk about the objective of dialogue between citizens and institutions. We therefore consider that this need of multiple channels of participation is only addressed for this objective, even though it is relevant for the other objectives identified.

### 4.3.3 Creation of opinion groups or neighborhood councils

The possibility for citizens to constitute groups depending on different characteristics (e.g., interests, place of residence, etc.) is often cited by our interviewees as one of their objectives. These groups are typically communities of people producing common proposals – at the demand of an institution or at their own initiative – on a given topic. In the case of Interviewee #2, the objective of their platform was to “mimic the functioning of [their] neighborhood councils”, i.e. to give the opportunity to citizens to enter a local group to submit proposals on the development of a given area. By placing emphasis on existing types of groups, such as neighborhood councils, the potential for contributors to form associations based on alternative criteria (e.g., interest or expertise in a given topic) is diminished. Interviewee #5 explains that “in Madrid, there was already citizen participation but it was an offline one and more oriented to already defined groups and associations”. The mean they chose to allow the expression of a more diverse panel of contributors was “to let citizens participate individually”, but other possibilities have been explored, in particular by Interviewee #1’s platform:

> We have a system to host citizen initiatives on our platform. Let's say I am a citizen, I have an initiative and I want to gather other citizens around my initiative. I can create an “embassy” on the city's participatory platform to find people. The city advertises the initiatives, we present them during a citizen participation council and people can decide that an initiative will be supported by the city.

The expected output of these platforms is not by itself a contribution from citizens to policies but the creation of groups of citizens according to their topics of