# Inference of social media opinion trends in 2022 Italian elections

Simon Zollo, Matteo Cinelli, Gabriele Etta, Roy Cerqueti, Walter Quattrociocchi

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Simon Zollo, Matteo Cinelli, Gabriele Etta, Roy Cerqueti, Walter Quattrociocchi. Inference of social media opinion trends in 2022 Italian elections. Expert Systems with Applications, 2025, 269, pp.126377. 10.1016/j.eswa.2024.126377. hal-05109451

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Inference of social media opinion trends in 2022 Italian elections

Simon Zollo a,*, Matteo Cinelli b, Gabriele Etta b, Roy Cerqueti a,c, Walter Quattrociocchi b

ARTICLE INFO

Keywords:

Network science

Label propagation

Political elections

Opinion mining

A B S T R A C T

Social media platforms play a significant role in political discourse, often serving as tools for political actors to disseminate partisan narratives, frequently encapsulated in concise slogans presented as hashtags. In this paper, we present a novel systematic framework leveraging network science tools and clustering algorithms to discern the political orientations of posts through their associated hashtags, that can be used in the context of opinion dynamics. Our results show that by applying this framework within the context of the 2022 Italian Elections, we successfully quantify the online activity of political coalitions and their supporters *pre* and *post-election*. By analyzing labeled posts derived from this framework we find a surge in user activity leading up to the election, followed by a pronounced decline afterward. Moreover, we note a remarkable shift in engagement toward the winning coalition *post-election*. Interestingly, at the coalition level, our findings reveal an inverse correlation between posting activity and the level of engagement received on social media platforms. Finally, a rank-size analysis of publication patterns among supporters during the *pre-election* period highlighted comparable trends in content generation across coalitions.

1. Introduction

Social media platforms have shaped how users consume infor-
mation, entertain themselves, and connect with other peers (Brown,
Broderick, & Lee, 2007; Kahn & Kellner, 2004; Quattrociocchi, Cal-
darelli, & Scala, 2014). The microblogging features and the decen-
tralized paradigm that characterize these platforms contributed to
an unprecedented content diffusion, making social media one of the
primary sources of information (Pepitone, 2010). Compared to tra-
ditional news providers, social media allow users to quickly join a
variety of debates, promoting the emergence of new ideas (Criado,
Sandoval-Almazan, & Gil-Garcia, 2013) and becoming rapidly aware
of many topics (Etta et al., 2023). Despite the potential benefits,
these platforms are affected by the presence of several antisocial
behaviors, such as the spreading of misinformation (Del Vicario et al.,
2016) and fake news (Vosoughi, Roy, & Aral, 2018; Wang, Wang,
& Han, 2022), the use of toxic speech (Avalle et al., 2024; Cheng,
Bernstein, Danescu-Niculescu-Mizil, & Leskovec, 2017; Quattrociocchi,
Etta, Avalle, Cinelli, & Quattrociocchi, 2022; Saveski, Roy, & Roy,
2021), and the emergence of polarization (Bail et al., 2018) and echo
chambers (Cinelli, De Francisci Morales, Galeazzi, Quattrociocchi, &
Starnini, 2021). These phenomena are usually detected after computing
relevant measures related to users' activity including their stance, that

is the expressions of the speaker's standpoint and judgment toward a
given proposition (Biber & Finegan, 1988). The study of people's stance
and their translation into voting intention and action that can impact
communities (Etta et al., 2022; Romero, 2021; Roosevelt, 2018; Zollo,
Baronchelli, Betsch, Delmastro, & Quattrociocchi, 2024) has attracted
growing attention in the literature. Indeed, the topic of stance detection
has been studied through different approaches. In general, it has been
demonstrated how, in stance detection tasks, network-based approach
outperforms their content-based counterparts in several comparative
studies (ALDayel & Magdy, 2021; Darwish, Magdy, & Zanouda, 2017;
Dong, Sun, Wang, Gu, & Zhong, 2017; Lahoti, Garimella, & Gionis,
2018; Magdy, Darwish, Abokhodair, Rahimi, & Baldwin, 2016; Wong,
Tan, Sen, & Chiang, 2016). Furthermore, Fernandez de Landa, Zubiaga,
and Agerri (2023) observed that a significant portion of research
aimed at inferring political stances have predominantly focused on
binary classifications between the two prevailing parties or ideolo-
gies (Barberá, 2015; Conover, Gonçalves, Ratkiewicz, Flammini and
Menczer, 2011; Conover, Ratkiewicz et al., 2011; Garimella & Weber,
2017; Pennacchiotti & Popescu, 2011a; Xiao, Song, Xu, Ren, & Sun,
2020), thereby exhibiting a notable absence of applicability within
multi-party contexts.

<footnote>a Department of Social Sciences and Economics, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, 00185, Italy</footnote>

<footnote>b Department of Computer Science, Sapienza University of Rome, Viale Regina Elena 295, Rome, 00161, Italy</footnote>

<footnote>c GRANEM, Université d'Angers, SFR Confluences, Angers, F-49000, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: simon.zollo@uniroma1.it (S. Zollo), matteo.cinelli@uniroma1.it (M. Cinelli), gabriele.etta@uniroma1.it (G. Etta),
roy.cerqueti@uniroma1.it (R. Cerqueti), walter.quattrociocchi@uniroma1.it (W. Quattrociocchi).</footnote>
To address this challenge, in this paper, we propose a novel sys-
tematic framework for inferring users' political leanings based on the
Label Propagation Algorithm (LPA). This graph-based semi-supervised
learning technique identifies content from similar coalitions based on
hashtags contained in posts. This framework is applied in the context
of the 2022 Italian Elections on two social media platforms – Twitter
(now X) and Facebook – to evaluate the prominence of the political
online discourse in a multi-party system before and after the electoral
event. The analysis unveils an increase in user activity in the period
leading up to the election date, followed by a sudden drop. We also note
that, despite reduced posting activity, the winning party (i.e. the right-
wing one) received more engagement on both social media platforms.
Finally, by conducting a rank-size analysis, we identify comparable
publication patterns among supporters during the pre-election period,
with only the most active group of each platform behaving differently.
Conversely, substantial difference were found in the post-election phase.

This study introduces different innovative contributions. First, it
presents a novel systematic framework for determining the political
leaning of posts through hashtags. Additionally, it applies the frame-
work to an unexplored context, thereby providing novel insights into
political dynamics and online engagement.

The rest of the paper is organized as follows. In Section 2 we describe the existing literature and contextualize the current study within the field. In Section 3 we describe the methodologies employed to perform the data collection and analysis proposed in this work. In this Section, we illustrate some notions of network science (Section 3.2), describe the process used to infer the political leaning of posts (Section 3.3), and characterize the theoretical background of the rank-size analysis (Section 3.4). Section 4 outlines the study results, describing the analysis process and applying our method to two different social media platforms. Section 5 discusses the research comprehensively, emphasizing the main findings and finally focusing on possible future research.

## 2. Literature review

With the rise of social media platforms, numerous studies have
been conducted to explore online social dynamics. In this context, Del
Vicario et al. (2016) examined the spread of misinformation, iden-
tifying selective exposure to content as the primary driver behind
the formation of echo chambers. Following this, additional research
focused on the diffusion (Vosoughi et al., 2018) and detection (Wang
et al., 2022) of fake news, frequently referring to politics (Bovet &
Makse, 2019). Similarly, another area of concern involves the use of
toxic speech (Cheng et al., 2017; Saveski et al., 2021), the structure of
toxic conversations (Quattrociocchi et al., 2022), and the persistence
of interaction patterns within this environment (Avalle et al., 2024).
Therefore, these phenomena contribute to the widening of polariza-
tion (Bail et al., 2018) and the reinforcement of echo chambers (Cinelli
et al., 2021).

Consequently, developing robust methodologies to address these
challenges is crucial, often relying on the detection of users' stance.,
i.e. the expressions of the speaker's standpoint and judgment toward
a given proposition (Biber & Finegan, 1988). As a result, interest
from researchers focused on the translation of people's points of view
into voting intentions and actions that potentially affect communi-
ties (Romero, 2021; Roosevelt, 2018), carrying attention to how social
media influences health behavior (Zollo et al., 2024). In this context, a
significant area of research focused on using sentiment analysis to infer
stances from textual data. For example, the study conducted by Park,
Ko, Kim, Liu, and Song (2011) applied sentiment analysis in the context
of political news, while Lai, Hernández Farías, Patti, and Rosso (2017)
employed analog techniques to examine political tweets.

Another major avenue of exploration involved detecting Twitter
users' stances through machine learning methods. Al Zamal, Liu, and
Ruths (2012) utilized latent attribute inference to ascertain users'

stances, while Cohen and Ruths (2013) identified some methodological
issues in these approaches. Boutet, Kim, and Yoneki (2012) proposed
an algorithm designed to identify users' political leanings relating to
their political parties, and Gómez-Suta, Echeverry-Correa, and Soto-
Mejía (2023) incorporated topic modeling for stance detection. How-
ever, Pennacchiotti and Popescu (2011b) leveraged machine learning
techniques based on network features, and Rao, Yarowsky, Shreevats,
and Gupta (2010) implemented a stacked SVM-based classification
algorithm. Finally, Zotova, Agerri, and Rigau (2021) contributed to
the field by semi-automatically creating labeled datasets for stance
detection on Twitter.

An additional line of research explores the use of community detec-
tion algorithms on networks. Conover, Ratkiewicz et al. (2011) used a
combination of network clustering algorithms and manually annotated
data to demonstrate polarization in political retweets. Bovet, Morone,
and Makse (2018) validated opinion trends on Twitter with national
polling aggregate during the 2016 U.S. elections by employing com-
munity detection on co-occurrence hashtag networks. Guerrero-Solé,
Corominas-Murtra, and López-González (2014) predicted the voters'
perception of the closeness between parties, and Wong et al. (2016)
introduced an innovative approach to infer political leanings using a
mathematical convex optimization framework. Lastly, correspondence
analysis and retweet interactions have been utilized to derive ideolog-
ical scores for Twitter users (Barberá, 2015; Falkenberg et al., 2022;
Flamino et al., 2023).

Therefore, in several comparative studies, results from network-
based approaches have proved to be particularly useful by outperform-
ing their content-based counterparts (ALDayel & Magdy, 2021; Darwish
et al., 2017; Dong et al., 2017; Lahoti et al., 2018; Magdy et al., 2016;
Wong et al., 2016). Consequently, recent network-based approaches re-
vealed how detecting political stances from online content with hashtag
co-occurrence networks can lead to prominent results (Bovet & Makse,
2019; Bovet et al., 2018). However, it has been observed (Fernandez de
Landa et al., 2023) that a significant portion of research aimed at
inferring political stances have predominantly focused on binary clas-
sifications between the two prevailing parties or ideologies (Barberá,
2015; Conover, Gonçalves et al., 2011; Conover, Ratkiewicz et al.,
2011; Garimella & Weber, 2017; Pennacchiotti & Popescu, 2011a; Xiao
et al., 2020), thereby exhibiting a notable absence of applicability
within multi-party contexts.

Aligning with this literature landscape, this study proposes a novel
systematic framework for inferring users' political leanings leveraging
network science tools, designed to be effective across different political
systems (i.e. bi- or multiparty systems).

3. Material and methods

3.1. Data

The dataset employed for this study consists of tweets and posts
collected from Twitter (now X) and Facebook, respectively, concern-
ing the 2022 Italian Elections. The data collection process ranges
from 25/08/22 to 25/10/22, capturing the online debate occurring
one month before and after the election date, which happened on
25/09/22. The collection of posts is performed through a keyword
search, listed in Table A.8, that includes political parties and leading
candidates' names. On Twitter, we leverage X Academical APIs to
obtain the data of interest, whilst, on Facebook, we use CrowdTangle
APIs. As a result, we obtain ~8.2M posts from Twitter and ~930K from
Facebook, which are filtered to retain only those containing at least
one hashtag. This process restricted the Twitter dataset to ~1.6M posts
and the Facebook one to ~291K posts (see Table 1 for a complete
description).

3.2. *Networks*

We will now provide an overview of some concepts in network
theory to enhance the understanding of our approach and methodology.
<table><caption>**Table 1**
Data breakdown of posts, with their corresponding interactions, collected from Facebook and X.</caption><thead><tr><th></th><th>Posts</th><th>Users</th><th>Likes</th><th>Shares</th><th>Comments</th><th>Hashtags</th></tr></thead><tbody><tr><td>Facebook</td><td>290 981</td><td>16 827</td><td>53 530 453</td><td>9 230 881</td><td>26 145 648</td><td>36 737</td></tr><tr><td>X</td><td>1 645 823</td><td>155 826</td><td>8 526 004</td><td>2 005 092</td><td>1 530 836</td><td>81 487</td></tr></tbody></table>

A graph $G = (V, E)$ is a data structure where $V$ denotes the set of $n$ nodes $i, j \in V$, and $E$ represents the set of $m$ edges $(i, j)$ connecting the nodes. The relationships between nodes can be represented using an adjacency matrix $A \in \mathbb{R}^{n \times n}$. In the case of unweighted graphs, where edges $e \in E$ may either be present or absent, we define $A_{ij} = 1$ if nodes $i$ and $j$ are connected, and $A_{ij} = 0$ otherwise. For weighted graphs, instead, we define $w: E \to [0, +\infty)$, such that $A_{ij} = w(i, j) > 0$ if nodes $i$ and $j$ are connected with a weight $w(i, j)$, and $A_{ij} = w(i, j) = 0$ otherwise. In this paper we only consider undirected graphs, ensuring that the matrix $A$ remains symmetrical.

In the case of unweighted graphs, the degree of a node $i$, denoted as $k_i$, represents the sum of connections to other nodes, expressed as $k_i = \sum_j A_{ij}$. Furthermore, the total number of edges in a graph $G$, denoted as $m$, is half the sum of all elements in the adjacency matrix, i.e., $m = \frac{1}{2} \sum_{ij} A_{ij}$.

Another instance we consider is the bipartite graph, i.e., a graph in which the vertex set $V$ is the union of two disjoint independent sets, $V'$ and $V''$, called the partitions of $G$. The equivalent of an adjacency matrix for a bipartite graph is a rectangular matrix called incidence matrix $B \in \mathbb{R}^{j \times g}$ where $B_{ij} = 1$ if nodes $i \in V'$ and $j \in V''$ are connected, so group $i$ contains participant $j$, $B_{ij} = 0$ otherwise. A bipartite graph can be projected onto one of its partitions by performing an operation called one-mode projection that can be formalized in terms of the product $P = B^T B$, in the case we are projecting onto the partition of size $j$, and $P = BB^T$ if we are projecting onto the partition of size $g$. The resulting matrix $P$, called the co-occurrence matrix, is symmetric and its elements $P_{ij}$ are nonnegative numbers that represent, in the case of off-diagonal elements, the number of shared edges of the nodes $i$ and $j$ to the partition of size $j$ or $g$, respectively.

### 3.2.1. Statistical filtering on networks

In complex networks, particularly in the context of co-occurrence networks, network filtering is a shared concern. Specifically, this challenge involves simplifying overly dense networks by systematically removing connections that are likely to be not significant (Coscia, 2021; Satuluri, Parthasarathy, & Ruan, 2011; Spielman & Teng, 2004). This process enhances the overall clarity and interpretability of the network structure, facilitating more effective analysis and understanding of its underlying dynamics. Network filtering may be essential when an excessive number of connections prevents to draw meaningful conclusions, affects computational capability, or introduces significant noise. Therefore, it is common to employ statistical filters to eliminate co-occurrences that may have occurred by chance, i.e., co-occurrences lacking statistical significance. Methods for assessing the statistical significance of links include a multitude of approaches, ranging from general ones (Coscia & Neffke, 2017; Massara, Di Matteo, & Aste, 2016; Serrano, Boguná, & Vespignani, 2009) to methods tailored specifically for (weighted) co-occurrence networks (Martinez-Romo et al., 2011), as in the context of the paper. Indeed, this family of statistical methods employed for discarding insignificant connections usually involves three steps: (1) defining a null model to represent a random network; (2) computing a *p-value* to assess the statistical significance of each edge relative to the null model; (3) discarding all edges with a *p-value* exceeding a chosen significance threshold.

Given the nature of our network, we choose as a filtering approach the one proposed by Martinez-Romo et al. (2011). It is based on a model that assesses the statistical significance of each edge by computing the probability $p$ of observing the corresponding number of co-occurrences

by chance, knowing the number of occurrences of the two hashtags and the total number of posts. A lower $p$ indicates a more significant connection. Finally, only the edges whose $p < p_0$, where $p_0$ is a determined selected threshold, are retained.

### 3.2.2. Label propagation algorithm

In network science, a community refers to a group of nodes more likely to be connected than nodes outside the group (Barabási, 2013). This definition implies that each community forms a locally dense and interconnected subgraph. The identification of these communities is carried out by community detection algorithms, with the most famous ones based on the maximization of modularity, such as the Greedy algorithm (Newman, 2004) and the Louvain algorithm (Blondel, Guillaume, Lambiotte, & Lefebvre, 2008). Despite their performance, these algorithms do not generally provide a proper way to create communities starting a set of nodes with specific attribute values. To overcome this issue, an algorithm that has gained prominence due to its efficiency, conceptual clarity, and ease of implementation is the Label Propagation Algorithm (LPA) (Raghavan, Albert, & Kumara, 2007). Inspired by the spread of epidemics, the LPA is a semi-supervised machine-learning approach that identifies communities by propagating node labels along edges, with each node selecting the community to which most of its neighbors belong. The labeling procedure carried out by the algorithm is the following. The algorithm starts by initializing every node with a unique label from the corresponding set. Then, it randomly sorts nodes in the network and, for each of them, assigns the label that occurs most frequently among its neighbors, breaking ties randomly. This labeling process is repeated until every node has a label that matches most of its neighbors. The algorithm converges when a consensus is reached among communities, identifying clusters whose nodes share the same label. A distinguishing feature of LPA compared to other community detection algorithms is its stopping criterion, which is a condition rather than an optimized measure. Such characteristic may result in instability, manifested as variance in community detection outcomes across different initializations (Evkoski, Mozetič, Ljubešić, & Kralj Novak, 2021). To mitigate this issue, one typical approach is to iterate the algorithm multiple times, as demonstrated in Evkoski et al. (2021) with the Louvain algorithm (Blondel et al., 2008). Indeed, by setting a conservative threshold $n$, nodes consistently assigned to the same cluster across iterations can be retained. Several variants of the original algorithm have been developed. In this paper, we employ an initialization step that allows the manual assignment of labels (corresponding to political parties) to specific nodes. These labels are then propagated to other nodes using an update rule. By doing so, we can control the number of the obtained communities and the integration of preliminary information in the community analysis. In general, the Label Propagation Algorithm is particularly suitable for our task as shown by prior studies employing it for identifying and inferring missing or unavailable labels (Garza & Schaeffer, 2019). This semi-supervised algorithm requires manual labeling of only a small subset of nodes, leveraging the underlying network structure for label inference. Additionally, its linear-time convergence ensures scalability for very large networks, enabling us to conduct multiple simulations to evaluate the stability of our results.

## 3.3. Content political leaning attribution

To assess the political leaning of the content circulating during the elections, we employ the following approach:

1. Creating a Co-occurrence Hashtags Network: we first create a weighted hashtag co-occurrence network, which is made up of nodes representing hashtags that are connected if they appear in the same post. The edge's weight represents the number of posts in which the two hashtags co-appeared.
2. **Network Filtering:** the obtained co-occurrence network may include hashtags that appeared in the same posts by chance. Therefore, we filter the network in order to retain only statistically significant links, as described in Section 3.2.1.

3. **Inferring Hashtag Leaning:** to determine the leaning of hashtags, we run the community detection algorithm based on the Label Propagation Algorithm, described in Section 3.2.2. The algorithm runs once provided with an initial set of hashtags (seeds) having a specific political orientation. The labels associated with the seeds will be propagated through the entire network by the algorithm that will retrieve a number of communities equal to the number of labels.

To mitigate the instability described in Section 3.2.2, we iterate the algorithm and assign each node to a community if it belongs to the same community at least n% of the times. Ultimately, the resulting network will contain a set of politically oriented hashtags in our network, organized into different clusters. The number of clusters will correspond to the number of unique labels manually assigned at the beginning of the whole process.

4. **Post Leaning Inference:** we perform a filtering operation on the posts, retaining only those text contents at least one politically oriented hashtag identified during the LPA phase. Since each post may contain multiple hashtags, the political leaning of a post is determined by the political label that accumulates the highest number of associated hashtags. In case two or more political coalitions have an equal number of hashtags, the post is labeled as *hybrid*.

### 3.4. Rank-size analysis

In the literature, rank-size analysis is a widely explored method when dealing with time series to inspect the properties of a large set of data (Cerqueti, Lupi, Pietrovito, & Pozzolo, 2022; Ficcadenti, Cerqueti, & Ausloos, 2019). In this landscape, in Zipf (1935, 1949) the power law and Pareto distribution with unitary coefficient were introduced to analyze the rank-size relationship in the linguistics field. The Zipf law has been advanced in various works introducing methodological modifications as in Popescu, Altmann, and Köhler (2010); in the Zipf-Mandelbrot law (ZML) (Fairthorne, 2005; Mandelbrot, 1961; Mandelbrot et al., 1953) and in the Lavalette law (see Lavalette, 1996), which have been proven to yield an amazing fit of rank-size relations, even when Zipf law fails to do it (see e.g. Ausloos & Cerqueti, 2016; Cerqueti & Ausloos, 2015). In this work, we apply the Zipf-Mandelbrot law, which is described as a function of the rank r:

$$y(r) = \frac{A}{(r + b)^c} \qquad (1)$$

where A, b, and c must be calibrated. In our specific case, the size of the rank-size analysis is determined by the number of posts published each day, with rank r = 1 representing the day with the highest publication frequency. Consequently, the estimated parameters interpretations for the case of (1) are the following: A increases as the maximum number of posts in a single day grows; a high value of b means a reduced deviation between the size at consecutive ranks — more evident for low ranks; the higher the value of c, the steeper the curve at low ranks. Indeed, the families of laws proposed after the first Zipf's paper have massively grown.

We also try the Universal rank-size law, described by Ausloos and Cerqueti (2016). The fitting of this function requires five parameters to be calibrated. To describe it, we need first to define the reduced variable w, as w ≡ (r − 1)/(r_M − 1), where r_M is the maximum rank.

Hence, the function is defined as:

$$y_2(w) = \eta (\phi + w)^{-\xi} [1 - w + \psi]^x \qquad (2)$$

where η represents a normalizing factor, parameters φ and ξ pertain to the lower ranks, with φ increasing as the deviation between the

<caption>Table 2</caption>

Topological features of Twitter and Facebook networks, including the number of non-isolated nodes N, the number of links E, the diameter size (d<sub>max</sub>), the average path length 〈d〉, the number of connected components n<sub>CC</sub> and the size, in percentage, of the biggest connected component, |CC<sub>1</sub>|.

<table><thead><tr><th>Social</th><th>Graph</th><th>N</th><th>E</th><th>d<sub>max</sub></th><th>⟨d⟩</th><th>n<sub>CC</sub></th><th>CC<sub>1</sub><sub>perc</sub></th></tr></thead><tbody><tr><td>Twitter</td><td>Original</td><td>65 944</td><td>554 693</td><td>4698</td><td>4.97</td><td>1810</td><td>0.93</td></tr><tr><td>Twitter</td><td>Filtered</td><td>63 677</td><td>213 932</td><td>4717</td><td>9.07</td><td>4900</td><td>0.89</td></tr><tr><td>Facebook</td><td>Original</td><td>34 033</td><td>342 226</td><td>129</td><td>3.92</td><td>739</td><td>0.93</td></tr><tr><td>Facebook</td><td>Filtered</td><td>33 386</td><td>191 661</td><td>384</td><td>6.42</td><td>1625</td><td>0.91</td></tr></tbody></table>

sizes at two consecutive (small) ranks, and a high value of ξ intensifies the increasing behavior of φ. Similarly, ψ and χ have analogous roles to χ and ξ, but apply to larger ranks. The interesting point about the Universal Rank Size Law is that it manages to fit rank-size distributions very well, even where other models struggle.

All analyses were conducted using the R programming environment, leveraging a range of packages for data manipulation, analysis, and visualization. Data uploading and preprocessing were performed with the packages *dplyr*, *readr*, *tibble*, *tidytext*, *tidyverse*, and *data.table*. Community detection and network analysis were carried out using the *igraph* package, while parameter estimation for model fitting was achieved with *minpack lm*. Data visualization was implemented using *ggplot2*, complemented by *gridExtra* and *cowplot* for improved layout and presentation.

## 4. Results

### 4.1. Network structure

We begin the analysis by creating a bipartite network between posts and hashtags. In this network, a hashtag *h* ∈ H is connected to a post *t* ∈ T if the hashtag is included in the post. By projecting the bipartite network onto the partition of hashtags, we derive the hashtag co-occurrence weighted network.

Table 2 describes the topological features resulting from the filtering operation on the co-occurrence network. We observe that this operation reduced the number of edges E by 61.4% on Facebook and 44% on Twitter. As a consequence, the number of non-isolated nodes N also decreased by 3.4% on Twitter and 1.9% on Facebook.

Next, we characterize the obtained filtered networks in terms of centrality measures. Fig. 1 describes how the distribution of node degrees, strength, and betweenness are long-tailed. This suggests the presence of a few hashtags that are widely used and central in the political discourse. Tables B.9 and B.10 display the 20 most important nodes according to degree, strength, and betweenness centrality in the Facebook and Twitter networks.

Finally, we analyze the tendency of nodes to cluster together. Results show a global clustering coefficient score of 0.22 for Facebook and 0.05 for Twitter, whilst for the local clustering coefficient, whose distributions are described in Fig. 2, a bimodal nature has been observed, with their highest peaks close to 1. These findings together suggest that both networks can contain clusters of hashtags that can be easily separated, providing support for applying a community detection approach.

### 4.2. Label Propagation Application

To assess the political orientation of posts, we leverage the clustering nature of the hashtag co-occurrence network by applying the LPA, described in Section 3.2.2. By doing so, each node is labeled with the name of one of the four coalitions that ran the 2022 Italian Elections, namely the Right, Left, Center, and Five-Star Movement coalitions. The algorithm requires an initial set of labeled hashtags, created manually to include hashtags predominantly associated with each coalition (see
<figure><img src="image_3.png" /><figcaption>Fig. 1. Degree (A, D), Strength (B, E), and Betweenness (C, F) of network distributions from Facebook (upper) and Twitter (lower).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Local clustering coefficient distributions for filtered Facebook (A) and Twitter (B) graphs.</figcaption></figure>

Tables C.11 and C.12 for the complete list).

To retain only those hashtags with a robust label across runs of the LPA, we impose a value of *n* equal to 90%, indicating that a labeled hashtag must be consistently associated with the same label at least 90% of the times (Tables 3 and 4 provide comprehensive results from applying different thresholds to the resulting sets). As a result of this process, we identify 1699 oriented hashtags in the Twitter network and 3818 oriented hashtags in the Facebook network. In order to provide a visual example of the outcome, Fig. 3 displays the Twitter co-occurrence network with the obtained labels. The hashtags and edges are colored according to the detected communities, and their size is proportional to their degree.

To assess the algorithm's performance, we randomly remove the labels from 10 initial hashtags of each coalition and run the algorithm, repeating this process 10 times. The labels assigned by the LPA to these hashtags are then compared with their true values, allowing us to compute the accuracy for each coalition in a single iteration. Table 5 summarizes the average coalition accuracy across all iterations, resulting in an average accuracy of 85% on Facebook and 79.37% on Twitter. This result demonstrates the algorithm's capability to correctly

<caption>Table 3</caption>

Number of Labeled Hashtags, resulting from the threshold application in Twitter co-occurrence network, for each coalition.

<table><thead><tr><th>Threshold value</th><th>n<sub>Left</sub></th><th>n<sub>FiveStarMovement</sub></th><th>n<sub>Center</sub></th><th>n<sub>Right</sub></th><th>n<sub>Total</sub></th></tr></thead><tbody><tr><td>90%</td><td>187</td><td>1038</td><td>118</td><td>356</td><td>1699</td></tr><tr><td>85%</td><td>193</td><td>1118</td><td>645</td><td>367</td><td>2323</td></tr><tr><td>80%</td><td>194</td><td>1268</td><td>659</td><td>380</td><td>2501</td></tr><tr><td>75%</td><td>201</td><td>1490</td><td>681</td><td>407</td><td>2779</td></tr><tr><td>70%</td><td>220</td><td>1699</td><td>682</td><td>430</td><td>3031</td></tr></tbody></table>

identify hashtags despite the multiparty scenario, thereby illustrating its generalizability.

### 4.3. Coalition engagement and supporters activity

We continue the analysis by quantifying the political orientation of posts. Using the set of labeled hashtags from the LPA, we are able to classify the political orientation of 39 837 posts on Facebook and 115 587 on Twitter. To assess the reliability of this classification, we
<figure><img src="image_5.png" /><figcaption>Fig. 3. Twitter co-occurrence hashtag network classification.</figcaption></figure>

<table><caption>**Table 4**
Number of Labeled Hashtags, resulting from the threshold application in Facebook co-occurrence network, for each coalition.</caption><thead><tr><th>Threshold value</th><th>N<sub>Left</sub></th><th>N<sub>FiveStarMovement</sub></th><th>N<sub>Center</sub></th><th>N<sub>Right</sub></th><th>N<sub>Total</sub></th></tr></thead><tbody><tr><td>90%</td><td>864</td><td>795</td><td>440</td><td>1719</td><td>3818</td></tr><tr><td>85%</td><td>999</td><td>904</td><td>488</td><td>1788</td><td>4179</td></tr><tr><td>80%</td><td>1096</td><td>969</td><td>521</td><td>1811</td><td>4397</td></tr><tr><td>75%</td><td>1288</td><td>1030</td><td>606</td><td>2048</td><td>4972</td></tr><tr><td>70%</td><td>1433</td><td>1145</td><td>726</td><td>2341</td><td>5645</td></tr></tbody></table>

<table><caption>**Table 5**
Average precision scores were calculated from the iterative application of the Label Propagation Algorithm, where the initial nodes of each coalition were randomly unlabeled.</caption><thead><tr><th>Social</th><th>Left</th><th>Five Star Movement</th><th>Center</th><th>Right</th><th>Total</th></tr></thead><tbody><tr><td>Facebook</td><td>93%</td><td>100%</td><td>63%</td><td>83%</td><td>85%</td></tr><tr><td>Twitter</td><td>52.5%</td><td>100%</td><td>95%</td><td>70%</td><td>79.37%</td></tr></tbody></table>

scrutinized the labels assigned to posts shared by accounts representing political parties and their representatives, whose political orientation is clearly expressed. Among the eligible posts, 96.7% of Facebook posts and 99.7% of Twitter posts were correctly associated with their respective political orientations, providing evidence of the accuracy of the proposed approach.

Next, the classified posts are used to quantify the resonance that parties produced—expressed as the number of published posts—and that of their supporters—expressed through the interactions collected by posts. Results in Fig. 4 describe an increasing trend in the content circulating before Election Day, which occurred on September 25, 2023, with an average number of posts equal to 1129 on Facebook and 3269 on Twitter. After that date, both platforms observed a decrease in overall activity, resulting in an average number of posts equal to 123 on Facebook and 505 on Twitter. Despite the significance of voting days in driving content production by parties and their supporters (Buccoliero, Bellio, Crestini, & Arkoudas, 2020; Caldarelli et al., 2014; Chaudhry et al., 2021), the two platforms exhibited different peak dates. On Facebook, the highest activity levels were recorded in conjunction with Election Day, while on Twitter, the peak appeared on September 17, 2023, coinciding with a rally event organized by the Right coalition (Guillou, 2023).

Regarding the volumes produced by coalitions, we observe variations depending on the considered platform. The Right coalition led the supporting activity on Facebook, with 17 656 posts, followed by the Five-Star Movement and the Left coalition. The Center coalition produced the

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Evolution of the number of posts published by supporters of the different parties on Facebook (Upper) and Twitter (Lower). The inset represents the total number of posts published by supporters of the different parties on each platform.</figcaption></figure>

lowest activity, with a total of 4740 posts. Conversely, on Twitter, the Five-Star Movement generated the highest levels of supporting activity, with 88 011 posts, in contrast to the Left coalition, which recorded the lowest activity levels, with 2925 posts.

Concerning the supporters' activity, results described in Fig. 5 report a twofold scenario with the Left coalition and the Five-Star party supporters producing the highest interaction levels. On Twitter, despite the lowest posting activity observed in Fig. 4, the Left coalition produced 205% more interactions than posts from the Five-Star Movement. Similarly, on Facebook, where the Right party registered the highest news volume, the Five-Star Movement coalition supporters collected the highest interaction levels, surpassing those produced by the Right party with a 224.7% increase. These findings indicate how, in the electoral debate, the most engaging posts were not those published by the most active groups.
<figure><img src="image_8.png" /><figcaption>Fig. 5. Mean of main engagement indicators in Facebook: (A) Total Interactions (B) Likes (C) Comments (D) Shares. The error bars are calculated as the standard deviation of bootstraps as wide as the numerosity of the smallest group (i.e. 2925 in Twitter Dataset and 4740 in Facebook).</figcaption></figure>

## 4.4. Evaluating online presence across parties

We pursue the analysis by quantifying the engagement activity of supporters from different coalitions before and after the election date. Results from Fig. 6 show an average decrease in activity of 11.7% on Facebook and 1.87% on Twitter, aligning with the posting trends observed in Fig. 4. This shift indicates that electoral campaigns produce a temporary increase in user activity, which subsides after the elections rather than being the result of a sustained influence. Despite the general decrease, supporters of the *Right* coalition exhibited an increase in the engagement their posts produced, with a 7.08% rise on Facebook and a 31.53% rise on Twitter. This increase may be linked to their electoral victory, which naturally translates into higher news coverage than their counterparts. Conversely, the highest reduction in engagement was observed in posts concerning the *Left* coalition, with a decrease of 30.50% on Facebook and 11.54% on Twitter. This suggests that losing parties may experience a general loss of interest and support following the elections.

## 4.5. Assessing coalition posting patterns through rank-size analysis

Finally, to further explore the publication patterns of supporters from various political parties, we conducted a rank-size analysis.

Specifically, the number of posts published is the size of the rank-size analysis. Each day is ranked by the number of posts published, so the rank $r = 1$ denotes the day with the highest publication frequency. Hence, as the rank number of a day increases, the quantity of published posts decreases.

Given the substantial disparities in post volumes, we identified two regimes: pre-voting and post-voting.

Consequently, we implemented a best-fit procedure to evaluate whether the size frequency $y$ quantified as a function of the ranks $r$. All fitting procedures utilized in this study were performed using the Levenberg-Marquardt algorithm (Levenberg, 1944; Lourakis et al., 2005; Marquardt, 1963).

After a visual inspection of the data, two different fit functions were considered for the two distinct regimes. We consider the Universal Rank Size Law, in Eq. (2), as a best-fit function for the pre-voting regime and

the Zipf-Mandelbrot Law (ZML), in Eq. (1), for the post-voting regime, considering the differences in the trends and in the volume of published posts.

Fig. 7 displays the best fits, showing the tendency of trends within their respective regimes to exhibit similar patterns. Therefore, to gain a deeper understanding of these patterns and discern their distinctions, we reported the estimated coefficients in Tables 6 and 7.

Table 6 illustrates the estimated coefficients according to the Universal Rank Size law (i.e. Eq. (2)), further described in Section 3.4. Firstly, by considering the coefficient of determination ($R^2$), we can observe that our data exhibits a strong fit. During the pre-election period, the parameters for all curves behave similarly, except for a few differences. Actually, the curves for the *Right* supporters on Facebook and the *Five-Star Movement* supporters on Twitter diverged from the rest. Specifically, the parameter $\psi$ differed for the former, while both $\psi$ and $\chi$ differed for the latter. These parameters shed light on the behavior of the high ranks. Since these two groups were the most active on their respective platforms, it is reasonable to explain their behavior as distinct. Therefore, we can conclude that, except for higher ranks, the behavior of all groups was quite similar during the pre-election phase.

Next, Table 7 outlines the estimated parameters concerning the post-vote period. These parameters pertain to Eq. (1), defined in Section 3.4. Our particular focus is on parameter $c$, representing the concavity of the curves.

Given the noticeable differences in values observed between the two social media platforms, we will analyze them separately. On Facebook, the curve for the *Center* coalition demonstrates a higher value of $c$ compared to other groups, indicating a more pronounced drop in activity. Turning to Twitter, we note that supporters from the *Left*, *Center*, and *Right* groups even display significant values in the $c$ parameter, especially in the case of the *Left* group. Nevertheless, the *Five-Star Movement* shows a lower value, suggesting a less steep drop in activity. Finally, we are once again satisfied with the goodness of fit, as described by $R^2$.

# 5. Conclusion and future work

In this paper, we introduce a framework that employs network science tools and clustering algorithms to discern the political orientations
<figure><img src="image_9.png" /><figcaption>Fig. 6. Comparison of engagement in Twitter before and after the elections: (A) Total Interactions (B) Likes (C) Replies (D) Retweets and Quotes.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 7. Semi log plot of the number of Posts published daily during the election; the days are ranked by their decreasing “order of importance” according to the number of posts published on that specific day. We distinguished two regimes, Pre Voting and Post Voting, fitting parameters according to the Universal Rank Size Law in the first regime (Pre Voting) and according to the Zipf Mandelbrot function in the second regime (Post Voting). Best fit parameters and R² are shown in Table 6 and in Table 7.</figcaption></figure>

<caption>Table 6</caption>

Universal rank size law best fit parameters and R² of the daily number of posts in the period before elections. The parameters pertain to the displaced data as visualized in Fig. 7, referring to trends in Fig. 4.

<table><thead><tr><th></th><th></th><th>η</th><th>φ</th><th>ε</th><th>ψ</th><th>χ</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="5">Facebook</td><td>Left</td><td>2.366608 · 10<sup>2</sup></td><td>0.00003819415</td><td>0.08072715</td><td>0.0001204117</td><td>0.2269885</td><td><strong>0.971</strong></td></tr><tr><td>Five Star Movement</td><td>3.64527 · 10<sup>2</sup></td><td>0.0001104461</td><td>0.04183405</td><td>0.01847138</td><td>0.3835194</td><td><strong>0.984</strong></td></tr><tr><td>Center</td><td>1.621784 · 10<sup>2</sup></td><td>2.133530 · 10<sup>-15</sup></td><td>0.01529671</td><td>0.0002905244</td><td>0.2917935</td><td><strong>0.968</strong></td></tr><tr><td>Right</td><td>4.074573 · 10<sup>2</sup></td><td>0.002127046</td><td>0.05126915</td><td>1.110089</td><td>0.3381408</td><td><strong>0.993</strong></td></tr><tr><td>Total</td><td>1.224597 · 10<sup>3</sup></td><td>0.00004956286</td><td>0.05189751</td><td>0.01319752</td><td>0.1736156</td><td><strong>0.991</strong></td></tr><tr><td rowspan="5">Twitter</td><td>Left</td><td>59.778198283</td><td>0.026441278</td><td>0.397834443</td><td>0.002598064</td><td>0.078505500</td><td><strong>0.991</strong></td></tr><tr><td>Five Star Movement</td><td>46.791988828</td><td>0.002256133</td><td>0.072659131</td><td>3.649538855</td><td>2.706282148</td><td><strong>0.985</strong></td></tr><tr><td>Center</td><td>214.05997024</td><td>0.15419573</td><td>0.33350466</td><td>0.07219817</td><td>0.29691447</td><td><strong>0.991</strong></td></tr><tr><td>Right</td><td>5.019772 · 10<sup>2</sup></td><td>0.4726048</td><td>0.4907932</td><td>0.001879005</td><td>0.06724535</td><td><strong>0.991</strong></td></tr><tr><td>Total</td><td>1.654959 · 10<sup>2</sup></td><td>0.006792516</td><td>0.09738890</td><td>3.921840</td><td>1.925319</td><td><strong>0.992</strong></td></tr></tbody></table>
<caption>**Table 7**</caption>

Zipf-Mandelbrot Best fit parameters and R² of the daily number of posts in the period after elections. The parameters pertain to the displaced data as visualized in Fig. 7, referring to trends in Fig. 4.

<table><thead><tr><th></th><th></th><th>c</th><th>b</th><th>A</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="5">Facebook</td><td>Left</td><td>0.3294840</td><td>-0.9328802</td><td>27.8843986</td><td><strong>0.956</strong></td></tr><tr><td>Five Star Movement</td><td>0.3462710</td><td>-0.9071212</td><td>77.2310407</td><td><strong>0.977</strong></td></tr><tr><td>Center</td><td>0.6894981</td><td>0.2078114</td><td>93.9028439</td><td><strong>0.955</strong></td></tr><tr><td>Right</td><td>0.3721935</td><td>-0.6479149</td><td>108.2542287</td><td><strong>0.973</strong></td></tr><tr><td>Total</td><td>0.3413722</td><td>-0.8667896</td><td>247.6353511</td><td><strong>0.985</strong></td></tr><tr><td rowspan="5">Twitter</td><td>Left</td><td>1.9808205</td><td>0.1841971</td><td>106.4586304</td><td><strong>0.994</strong></td></tr><tr><td>Five Star Movement</td><td>0.6847261</td><td>0.1294088</td><td>1450.5355827</td><td><strong>0.987</strong></td></tr><tr><td>Center</td><td>1.1146132</td><td>-0.1854685</td><td>63.2716731</td><td><strong>0.988</strong></td></tr><tr><td>Right</td><td>1.247281</td><td>1.985937</td><td>721.992724</td><td><strong>0.991</strong></td></tr><tr><td>Total</td><td>0.6444796</td><td>-0.1957769</td><td>1459.8837825</td><td><strong>0.983</strong></td></tr></tbody></table>

of posts. We first apply this framework to classify the political orientation of the hashtags used during the 2022 Italian Elections debate on Facebook and X. Then, we classify the posts from the resulting hashtags to analyze the shifts in opinion trends between the pre and post-electoral period on both platforms. Our results show that our methodology can effectively identify clusters of hashtags referring to different coalitions, even within a multi-party system, achieving an average precision of 85% on Facebook and 79.37% on Twitter. Analyzing opinion trends derived from posts labeled with their hashtags, we observe an increasing posting activity by political parties leading up to the election, followed by a sharp decline afterward. Interestingly, despite high posting activity, coalitions with the most posts did not necessarily receive equivalent levels of engagement from their supporters. Conversely, parties with fewer posts achieved higher engagement levels. Furthermore, we illustrate how the winning party’s victory resulted in increased engagement on their posts. Conclusively, by performing a rank-size analysis, we identified similar publication patterns among supporters during the pre-election period, with differences only among the most active groups on each platform. However, in the post-election phase, substantial disparities emerged. Despite the advancement, our study presents different limitations. Firstly, labeling initial hashtags requires careful consideration, as correctly assigning political leanings to them can be challenging. Additionally, our methodology has minimum requirements, as outlined in Bovet et al. (2018), necessitating widespread and divisive discussion topics and distinct hashtag clusters in co-occurrence networks.

Our research contributes to developing new methodologies for stance detection using hashtag co-occurrence networks, applicable to various political systems, whether bi- or multi-party. Future research could explore hashtag co-occurrence networks on social media and their applications in analyzing various social phenomena.

### CRediT authorship contribution statement

**Simon Zollo:** Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing. **Matteo Cinelli:** Conceptualization, Methodology, Formal analysis, Data curation, Validation, Writing – original draft, Writing – review & editing. **Gabriele Etta:** Methodology, Software, Data curation, Investigation, Writing – original draft. **Roy Cerqueti:** Supervision, Methodology, Validation, Writing – original draft, Writing – review & editing. **Walter Quattrociocchi:** Conceptualization, Supervision, Methodology, Validation, Project administration, Writing – original draft, Writing – review & editing.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

<caption>**Table A.8**</caption>

List of keywords employed in the data collection for the 2022 Italian Elections.

<table><thead><tr><th>Keywords</th></tr></thead><tbody><tr><td>Calenda, Salvini, Meloni, Letta, Bonino, Fratoianni, Conte, DiMaio, Renzi, Paragone, Berlusconi, Civati, Draghi, ElezioniPolitiche2022, Elezioni2022, ElezioniPolitiche, CampagnaElettorale, CampagnaElettorale2022, Azione, LegaNord, Lega, fratelliditalia, fdi, pd, pdnetwork, partitodemocratico, PiuEuropa,sinistralitaliana, fivestar, movimento5stelle, impegnocivico, italiaviva, italexit, forzaitalia, possibileit</td></tr></tbody></table>

## Appendix A. Keyword terms employed in data collection

Table A.8 reports the terms used during data collection procedure.

## Appendix B. Network structure

Tables B.9 and B.10 report the hashtag names of the 20 nodes with the highest centrality scores computed on Twitter and Facebook co-occurrence networks. The measures considered in this work are the *Degree*, *Strength*, *Betweenness* and *Closeness*.

<caption>**Table B.9**</caption>

Top 20 nodes with the highest centrality scores computed on the Twitter hashtag co-occurrence network.

<table><caption>**Table B.10**</caption><thead><tr><th></th><th>Degree</th><th>Strength</th><th>Betweenness</th></tr></thead><tbody><tr><td>1</td><td>italia</td><td>ucraina</td><td>italia</td></tr><tr><td>2</td><td>elezioni</td><td>nato</td><td>elezioni</td></tr><tr><td>3</td><td>elezionipolitiche22</td><td>usa</td><td>governo</td></tr><tr><td>4</td><td>governo</td><td>gas</td><td>elezionipolitiche22</td></tr><tr><td>5</td><td>politica</td><td>sanzioni</td><td>putin</td></tr><tr><td>6</td><td>putin</td><td>ue</td><td>politica</td></tr><tr><td>7</td><td>25settembre</td><td>energia</td><td>25settembre</td></tr><tr><td>8</td><td>dimaio</td><td>nazisti</td><td>governomeloni</td></tr><tr><td>9</td><td>governomeloni</td><td>governo</td><td>giorgiameloni</td></tr><tr><td>10</td><td>giorgiameloni</td><td>politica</td><td>dimaio</td></tr><tr><td>11</td><td>terzopol</td><td>news</td><td>russia</td></tr><tr><td>12</td><td>dallapartegiusta</td><td>giorgiameloni</td><td>terzopol</td></tr><tr><td>13</td><td>russia</td><td>italia</td><td>roma</td></tr><tr><td>14</td><td>elezionipolitiche</td><td>covid</td><td>dallapartegiusta</td></tr><tr><td>15</td><td>roma</td><td>enricoletta</td><td>sinistra</td></tr><tr><td>16</td><td>destra</td><td>politica</td><td>destra</td></tr><tr><td>17</td><td>sinistra</td><td>economia</td><td>elezionipolitiche</td></tr><tr><td>18</td><td>ucraina</td><td>casa</td><td>mattarella</td></tr><tr><td>19</td><td>news</td><td>radio</td><td>ucraina</td></tr><tr><td>20</td><td>trojan</td><td>casaitaliaradio</td><td>trojan</td></tr></tbody></table>

Top 20 nodes with the highest centrality scores computed on the Facebook hashtag co-occurrence network.

<table><thead><tr><th></th><th>Degree</th><th>Strength</th><th>Betweenness</th></tr></thead><tbody><tr><td>1</td><td>politica</td><td>25settembrevotolega</td><td>italia</td></tr><tr><td>2</td><td>italia</td><td>credo</td><td>politica</td></tr><tr><td>3</td><td>dallapartegiusta</td><td>politica</td><td>25settembre</td></tr><tr><td>4</td><td>25settembre</td><td>dallapartegiusta</td><td>dallapartegiusta</td></tr><tr><td>5</td><td>25settembrevotolega</td><td>italia</td><td>giorgiameloni</td></tr><tr><td>6</td><td>credo</td><td>news</td><td>25settembrevotolega</td></tr><tr><td>7</td><td>giorgiameloni</td><td>governo</td><td>roma</td></tr><tr><td>8</td><td>italy</td><td>notizie</td><td>credo</td></tr><tr><td>9</td><td>governo</td><td>25settembre</td><td>italy</td></tr><tr><td>10</td><td>italiasulserio</td><td>camera</td><td>lavoro</td></tr><tr><td>11</td><td>lavoro</td><td>informazione</td><td>governo</td></tr><tr><td>12</td><td>roma</td><td>giorgiameloni</td><td>giovani</td></tr><tr><td>13</td><td>alleanzaverdisinistra</td><td>terzopol</td><td>italiasulserio</td></tr><tr><td>14</td><td>giovani</td><td>italiasulserio</td><td>senato</td></tr><tr><td>15</td><td>ucraina</td><td>cronaca</td><td>centrodestra</td></tr><tr><td>16</td><td>terzopol</td><td>repubblica</td><td>territorio</td></tr><tr><td>17</td><td>centrodestra</td><td>scegli</td><td>milano</td></tr><tr><td>18</td><td>scegli</td><td>ambiente</td><td>sport</td></tr><tr><td>19</td><td>musica</td><td>giovani</td><td>scuola</td></tr><tr><td>20</td><td>senato</td><td>notizia</td><td>elezioni25settembre</td></tr></tbody></table>
## Appendix C. Labeled hashtags

See Tables C.11 and C.12.

## Data availability

Data will be made available on request.

<caption>**Table C.11**</caption>

List of hashtags from the Twitter co-occurrence network, labeled with respect to the political leaning, which served as input for the Label Propagation Algorithm on the network itself.

<table><thead><tr><th></th><th>Right-Leaning</th><th>Left-Leaning</th><th>Center</th><th>Five-Star Movement</th></tr></thead><tbody><tr><td>1</td><td>25settembrevotolega</td><td>25settembrevotopd</td><td>iovototerzopol</td><td>iovotom5s</td></tr><tr><td>2</td><td>credo</td><td>iovotopd</td><td>iovotoitaliaviva</td><td>iovotom5sconconte</td></tr><tr><td>3</td><td>iovotolega</td><td>uniamoci</td><td>iostoconrenzi</td><td>conteilmiopresidente</td></tr><tr><td>4</td><td>domenicavotolega</td><td>agendarobinhood</td><td>iovotoitaliasulsrerio</td><td>iovotoconte</td></tr><tr><td>5</td><td>iovotomeloni</td><td>iovotounionepopolare</td><td>iovotorenzi</td><td>avanticonconte</td></tr><tr><td>6</td><td>iovotosalvini</td><td>siamoilpd</td><td>vototerzopolo</td><td>iostodallapartegiusta</td></tr><tr><td>7</td><td>forzagiorgiameloni</td><td>iovotopiueuropa</td><td>iovotoilterzopolo</td><td>iostoconconte</td></tr><tr><td>8</td><td>iocicredo</td><td>il25settembrevotopd</td><td>iovotolucano</td><td>iovotomovimento5stelle</td></tr><tr><td>9</td><td>cuoreleghista</td><td>italiarestalibera</td><td>iostoalcentro</td><td>iovotomovimento5stelleconconte</td></tr><tr><td>10</td><td>legavinciamo</td><td>il25settembreiovotopd</td><td>iovotoitaliasulserio</td><td>cuoreecoraggio</td></tr><tr><td>11</td><td>vivalalega</td><td>difendiamolalibertà</td><td>iovotocalenda</td><td>noivotiamom5sconconte</td></tr><tr><td>12</td><td>25settembrevotalega</td><td>votapd</td><td>forzacalenda</td><td>iovotoconteuomogiustoperGovernare</td></tr><tr><td>13</td><td>iostoconsalvini</td><td>sceglipd</td><td>forzarenzi</td><td>conteilnostropresidente</td></tr><tr><td>14</td><td>venetoalsicuro</td><td>votopd</td><td>forzaterzopolo</td><td>iovoto5stelle</td></tr><tr><td>15</td><td>iovotogiorgiameloni</td><td>iovotopartitodemocratico</td><td>iovotorenziecalenda</td><td>iovotogiuseppeconte</td></tr><tr><td>16</td><td>votagiorgia</td><td>iosonemo</td><td>iovotomatorezenzi</td><td>iovotoconteuomogiustodallapartegiusta</td></tr><tr><td>17</td><td>domanivotolega</td><td>iovotopiueuropa</td><td>votaterzenzi</td><td>movimento5stelleunicasperanza</td></tr><tr><td>18</td><td>forzameloni</td><td>25settembrevotounionepopolare</td><td>votoazioneitaliavivacalenda</td><td>iovotoconteuomoonestodallapartegiusta</td></tr><tr><td>19</td><td>forzalega</td><td>iovotalleanzaverdisinistra</td><td>iovotocarlocalenda</td><td>unicovotoutilem5salsud</td></tr><tr><td>20</td><td>noiconsalvini</td><td>laforzadeiterritori</td><td>italiaalcentro</td><td>iovotocontedallapartegiusta</td></tr><tr><td>21</td><td>iostocongiorgia</td><td>unasindacainparlamento</td><td>terzopolosiamonoi</td><td>iovotocontepremier</td></tr><tr><td>22</td><td>iovotofdi</td><td>25settembrevotapd</td><td>votateterzopolo</td><td>movimento5stelleforever</td></tr><tr><td>23</td><td>25settembrevotomeloni</td><td>vinconoleidee</td><td>votaitaliaviva</td><td>iovoto5stelleconconte</td></tr><tr><td>24</td><td>25settembrevotofi</td><td>ioscelgopd</td><td></td><td>votom5s</td></tr><tr><td>25</td><td>iostoconlameloni</td><td>forzapd</td><td></td><td>votam5sconnoi</td></tr><tr><td>26</td><td>forzagiorgia</td><td>doratoalsenato</td><td></td><td>votatecontem5sdallapartegiusta</td></tr><tr><td>27</td><td>iostocongiorgiameloni</td><td>unagenerazioneavanti</td><td></td><td>iovotocontesempre</td></tr><tr><td>28</td><td>forzasilvio</td><td>25settembrevotoalleanzaverdisinistra</td><td></td><td>ioscelgom5s</td></tr><tr><td>29</td><td>il25settembrevotolega</td><td>iovotoavs</td><td></td><td></td></tr><tr><td>30</td><td>iovotoforzaitalia</td><td>25settembrevotoavs</td><td></td><td></td></tr><tr><td>31</td><td>iovotosilvio</td><td>ognivotovince</td><td></td><td></td></tr><tr><td>32</td><td>25settembrevotoforzaitalia</td><td>votiamopd</td><td></td><td></td></tr><tr><td>33</td><td>barrareilsimbolofdi</td><td>votapartitodemocratico</td><td></td><td></td></tr><tr><td>34</td><td>tralagente</td><td>votapiueuropa</td><td></td><td></td></tr><tr><td>35</td><td>unitisivince</td><td>votosinistra</td><td></td><td></td></tr><tr><td>36</td><td>votaborghi</td><td>votasinistraitaliana</td><td></td><td></td></tr><tr><td>37</td><td>votabarabotti</td><td>votapiueuropa</td><td></td><td></td></tr><tr><td>38</td><td>votalega</td><td>votereunionepopolare</td><td></td><td></td></tr><tr><td>39</td><td>domenicavotalega</td><td>votarepd</td><td></td><td></td></tr><tr><td>40</td><td>scegliilcoraggio</td><td>iosonodisinistra</td><td></td><td></td></tr><tr><td>41</td><td>votagiorgiameloni</td><td>vivailpd</td><td></td><td></td></tr><tr><td>42</td><td>iovotfratelliditalia</td><td>votaunionepopolare</td><td></td><td></td></tr><tr><td>43</td><td>unasceltadicampo</td><td>votatepd</td><td></td><td></td></tr><tr><td>44</td><td>iovotoilcentrodestra</td><td>votopiueuropa</td><td></td><td></td></tr><tr><td>45</td><td>vivamatteosalvini</td><td></td><td></td><td></td></tr><tr><td>46</td><td>votaforzaitalia</td><td></td><td></td><td></td></tr><tr><td>47</td><td>votamontemagni</td><td></td><td></td><td></td></tr><tr><td>48</td><td>iovotoberlusconi</td><td></td><td></td><td></td></tr><tr><td>49</td><td>votareadestra</td><td></td><td></td><td></td></tr><tr><td>50</td><td>votoforzaitalia</td><td></td><td></td><td></td></tr><tr><td>51</td><td>votanoimoderati</td><td></td><td></td><td></td></tr></tbody></table>
<caption>Table C.12</caption>

List of hashtags from the Facebook co-occurrence network, labeled with respect to the political leaning, which served as input for the Label Propagation Algorithm on the network itself.

<table><thead><tr><th></th><th>Right-Leaning</th><th>Left-Leaning</th><th>Center</th><th>Five-Star Movement</th></tr></thead><tbody><tr><td>1</td><td>25settembrevotalega</td><td>25settembrevotapd</td><td>iovotocalenda</td><td>avanticonte</td></tr><tr><td>2</td><td>25settembrevotoforzaitalia</td><td>25settembrevotopd</td><td>iovotoitaliasulserio</td><td>contepresidente</td></tr><tr><td>3</td><td>25settembrevotolega</td><td>agendarobinhood</td><td>iovotoitaliaviva</td><td>cuoreecoraggio</td></tr><tr><td>4</td><td>credo</td><td>difendiamolalibàt</td><td>iovotolucano</td><td>dajem5s</td></tr><tr><td>5</td><td>domanivotolega</td><td>il25settembreiovotopd</td><td>iovotorenzi</td><td>giuseppecontepresidente</td></tr><tr><td>6</td><td>domenicavotolega</td><td>il25settembrevotopd</td><td>iovotertzopolo</td><td>iostoconte</td></tr><tr><td>7</td><td>domenicavotolega</td><td>il25settembrevotoverdisinistra</td><td>italiaalcentro</td><td>iovotoconte</td></tr><tr><td>8</td><td>forzagiorgia</td><td>ioscelgopd</td><td>italiasulserio</td><td>iovotom5s</td></tr><tr><td>9</td><td>forzalega</td><td>iosonoemma</td><td>sardegnasulserio</td><td>iovotom5sconte</td></tr><tr><td>10</td><td>il25settembrevotolega</td><td>iovotopartitodemocratico</td><td>unvotoutilissimo</td><td>iovotomovimento5stelle</td></tr><tr><td>11</td><td>iocicredo</td><td>iovotopd</td><td>iostoconrenzi</td><td>iovotoorfeomazzella</td></tr><tr><td>12</td><td>iostocongiorgia</td><td>iovotopieuropa</td><td>votalitaliasulserio</td><td>presidenteconte</td></tr><tr><td>13</td><td>iostconsalvini</td><td>iovotopiueuropa</td><td>votaitaliaviva</td><td>movimento5stelleforever</td></tr><tr><td>14</td><td>iovotofdi</td><td>iovotounionepopolare</td><td></td><td>il25settembrevotam5s</td></tr><tr><td>15</td><td>iovotoforzaitalia</td><td>italiarestalibera</td><td></td><td></td></tr><tr><td>16</td><td>iovotofratellidalia</td><td>laforzadeiterritori</td><td></td><td></td></tr><tr><td>17</td><td>iovotolega</td><td>ognivotovince</td><td></td><td></td></tr><tr><td>18</td><td>oggivotolega</td><td>scegliletta</td><td></td><td></td></tr><tr><td>19</td><td>tralagente</td><td>sceglipd</td><td></td><td></td></tr><tr><td>20</td><td>unasceltadicampo</td><td>unagenerazionevanti</td><td></td><td></td></tr><tr><td>21</td><td>unitisivince</td><td>unasindacainparlamento</td><td></td><td></td></tr><tr><td>22</td><td>venetoalsicuro</td><td>uniamoci</td><td></td><td></td></tr><tr><td>23</td><td>votabarabotti</td><td>vinconoleidee</td><td></td><td></td></tr><tr><td>24</td><td>votaborghi</td><td>votapartitodemocratico</td><td></td><td></td></tr><tr><td>25</td><td>votaforzaitalia</td><td>votapd</td><td></td><td></td></tr><tr><td>26</td><td>votalega</td><td>votopd</td><td></td><td></td></tr><tr><td>27</td><td>votoforzaitalia</td><td>votapiueuropa</td><td></td><td></td></tr><tr><td>28</td><td>votamontemagni</td><td>votaunionepopolare</td><td></td><td></td></tr><tr><td>29</td><td>votanoimoderati</td><td></td><td></td><td></td></tr><tr><td>30</td><td>votagiorgia</td><td></td><td></td><td></td></tr><tr><td>31</td><td>votagiorgiameloni</td><td></td><td></td><td></td></tr><tr><td>32</td><td>vivalalega</td><td></td><td></td><td></td></tr></tbody></table>

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