EVENT REPORT

Report on the 1st Workshop on Implicit Author
Characterization from Texts for Search andRetrieval (IACT 2023) at SIGIR 2023
Marina Litvak

Irina Rabaev

Ricardo Campos

Shamoon College of Engineering
IsraelShamoon College of Engineering
Israel

INESC TEC & UBI
Portugallitvak.marina@gmail.com

irinar@ac.sce.ac.il

ricardo.campos@ubi.pt

Alı́pio M. Jorge

Adam Jatowt

INESC TEC & FCUP
Portugal

Univ. of Innsbruck
Austriaamjorge@fc.up.pt

jatowt@acm.org

Abstract
The first edition of the International Workshop on Implicit Author Characterization fromTexts for Search and Retrieval (IACT’23) was held on July 27th , 2023, in conjunction withthe 46th International ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR) in Taipei, Taiwan. To support both online and in-person participation,
the workshop was held as a hybrid event. Online participation was allowed using the EventX
platform. During the course of the day, about 15 attendees had the opportunity to follow
up and discuss the recent advances in topics related to identifying and extracting implicit
information about authors (e.g., human or AI) from texts and using it in IR tasks. Theworkshop program included two invited keynotes, four paper presentations, and the reportof the ColiE competition which was held as a part of the workshop1 . The proceedings of theworkshop are available online at https://ceur-ws.org/Vol-3477/.
Date: 27 July 2023.
Website: https://en.sce.ac.il/news/iact23.

1Introduction

The IACT workshop series was established with the primary objective of bringing together researchers from diverse but related fields such as Information Retrieval, Natural Language Processing, Information Extraction, Machine Learning, Artificial Intelligence, and Social Sciences,
to share the recent advances in their respective fields towards implicit author characterization.
Implicit author information in the text includes details about the author’s characteristics, text1

http://www.kaggle.com/competitions/colie

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context (time, place, intent, and impacts of writing), and emotional background [Balahur et al.,2012; Wei et al., 2021; Kuznetsov and Kozerenko, 2011; Lu et al., 2020; Tubishat et al., 2018;
Alswaidan and Menai, 2020; Heidari et al., 2020; Nemati, 2018]. This extracted data is valuable
for various information retrieval applications, including query understanding, document characterization, historical document analysis, literature analysis, improving query understanding,AI-generated content detection, forensic linguistics, and political and social studies, ultimately
enhancing knowledge acquisition and reasoning in search and retrieval.
Based on the recently rising interest of the research community in leveraging AI for text
generation, we organized the first edition of the IACT workshop, held as a hybrid event under theumbrella of SIGIR 2023. Figure 1 captures the organizers and a few participants of the workshop.

Figure 1. Organizers and some participants at the IACT 2023 Workshop coming together for a group
picture.

This report provides an overview of the main activities that took place during the workshop.
Despite the hybrid format, with both physical and virtual attendance options, the attendees’
enthusiastic engagement ensured that it was an intellectually stimulating and successful event.
Only four papers were accepted in the research track. In addition, seven enthusiastic teams from allaround the world participated in the Competition on Automatic Classification of Literary Epochs(ColiE), providing an opportunity to compare the performances of different models and systems forthis task. The final program comprised four papers, along with two invited keynote talks and thecompetition report. The IACT proceedings can be found at https://ceur-ws.org/Vol-3477/.

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2The ProgramThis section presents a brief overview of the research works presented at the workshop.

2.1

Keynotes

Prof. Mark Last from the Ben-Gurion University of the Negev, Israel, presented a studyof multilingual propaganda content on the web, initially focusing on monitoring terrorist website
visitors and later developing tools for summarizing untranslated content and detecting metaphoriclanguage. His presentation addressed the unique characteristics of terrorist-generated propaganda
in various languages and explored the challenges, motivations, and ethical foundations behind thisresearch.
The second keynote by Prof. Dr. Valia Kordoni from Humboldt University, Berlin, discussed the shift from traditional keyword-based search engines to new Artificial Intelligence-based
information access systems, which can generate personalized natural language responses. Thesesystems, based on Large Language Models (LLMs), offer advantages in fulfilling information
queries but also come with downsides. The talk examined the benefits and drawbacks of AIbased information access systems from both developers’ and users’ perspectives.

2.2

Full Papers Research Track

The papers in the full paper research track of the workshop spanned a variety of topics including
systematic analysis for selecting an optimal hierarchical order for multi-task architectures, exploring automatic tools for a vacancy or resume ranking, and automatic differentiation of human-vsAI-written texts.
The paper of Chen et al. [2023] focused on the selection of hierarchical orders in multi-taskarchitectures, a significant challenge in developing neural network architectures. They proposeda systematic methodology based on the statistical results of the Apriori algorithm to arrange
the order of co-training tasks. Their findings demonstrated that this approach can provide nearoptimal performance, significantly reducing the exploration times in multi-task scenarios. Themodels developed using this methodology surpass state-of-the-art performances in flu vaccination
intent prediction and music review sentiment analysis tasks, demonstrating its efficacy.
The work of Vanetik et al. [2023] explored the process of resume matching, which involves
comparing a candidate’s CV with a job description to assess their suitability for a position. The
study presented a new dataset of hi-tech resumes specially compiled for this task, together withground truth rankings. The authors evaluated this dataset with OKAPI BM25 and a neuralbaseline and suggested a new method for vacancy ranking that relies on the semantic vector
representation of texts. In addition, the study proposed an approach to simplify and enhanceexisting practices in resume vacancy matching, intending to streamline the recruitment process
for both employers and applicants.
Giorgi et al. [2023] characterized differences between truthful text written by humans, intentionally deceptive text written by humans, and inherently deceptive text written by state-of-theart language models (ChatGPT). Through examining the expression of thirteen psychologicallygrounded and fundamental human traits (e.g., personality and empathy) across hotel reviews,

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the researchers discovered that human texts have a wider variety of personality expressions thanChatGPT texts. Based on these observations, the authors trained a classifier using thirteen human traits to automatically discriminate between truthful and deceptive language. The results
suggest that AI and humans are behaviorally different when communicating about experiences.
Silva and Frommholz [2023] approached differentiating human-vs-AI-written text in scientificarticles from a multi-authorship perspective by investigating automatically generated abstracts.
The authors proposed a multimodal transformer that combines handcrafted stylometric featureswith deep learning-based text features to perform multi-author attribution. The effectiveness ofthe proposed approach was evaluated on a curated dataset of 1000 samples. According to theexperimental results, the multimodal transformer model combining stylometric and text features
significantly outperforms the BERT model.

2.3

The CoLiE Competition and Awards

The workshop ended with the CoLiE competition report and the announcement of the 1st and 2ndplace winners. The competition provided an opportunity to compare and evaluate the effectivenessof different classification methods to categorize texts into their respective literary epochs. Seventeams enthusiastically participated in the contest, with a total of 71 submissions. Each team chosethe two best submissions that counted toward the final rank.
First place was awarded to Tomasz Walkowiak, CLARIN-PL, Wroclaw University of Science
Technology, Poland. A second place was awarded to Pietro Maldini, an independent participantfrom Italy.
The competition report was published as a part of the IACT’23 proceedings. Several interestingobservations, some more expected and some less, were reported. In the future, we intend toorganize new editions of this competition with new tasks related to literary classification andimplicit information retrieval.

3Key Contributors

The Program Chairs were Marina Litvak, Irina Rabaev, Ricardo Campos, Alı́pio Jorge, and AdamJatowt. The proceedings were set up by Irina Rabaev and Marina Litvak. Shay Shabtay and Hugo
Sousa were the web and dissemination chairs, respectively.
Finally, we would like to acknowledge the effort and valuable contribution of the researchersand industry experts who have served on the Program Committee of the IACT’23 workshop. Ourthanks go to:• Alvaro Figueira, CRACS / INESC TEC and University of Porto• Anastasia Giachanou, Utrecht University• Antoine Doucet, University of La Rochelle• Anubhav Jangra, Indian Institute of Technology Patna• Brenda Salenave Santana, Universidade Federal do Rio Grande do Sul• Bruno Martins, IST and INESC-ID - Instituto Superior Técnico, University of Lisbon• Chaya Liebeskind, Jerusalem College of Technology• Evelin Amorim, Universidade Federal do Espı́rito Santo
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• Horacio Saggion, Universitat Pompeu Fabra• Ignatius Ezeanii, Lancaster University• Jonathan Schler Holon Institute of Technology (HIT)• Lei Li, Beijing University of Posts and Telecommunications• Lin Miao, Beijing Information Science & Technology University• Mahmoud El-Haj, Lancaster University• Mark Last, Ben-Gurion University of the Negev• Moreno La Quatra, Politecnico di Torino• Natalia Vanetik, Shamoon College of Engineering• Nuno Guimaraes, CRACS - INESC TEC• Satya Almasian, Heidelberg University• Shiva Pentyala, Salesforce AI• Sophie Krimberg, Shamoon College of Engineering• Sandra Vilas Boas Jardim, Instituto Politécnico de Tomar• Valia Kordoni, Humboldt University Berlin• Yang Zhang, Kyoto University• Yihong Zhang, Osaka University

4Concluding Thoughts

The goal of the IACT workshop was to unite participants with diverse backgrounds and expertisein order to collaboratively establish the direction for the emerging field of implicit author characterization from texts and distinguishing between human-generated and AI-generated texts. We
are delighted by the positive response from the research community highlighting the importanceof the topic.

Acknowledgements
Our gratitude goes to the SIGIR 2023 organizers for their assistance in facilitating the workshop,
as well as our exceptional keynote speakers, program committee members, paper authors, andparticipants whose contributions led to the success of the workshop. Ricardo Campos and Alı́pio
Jorge were financed by National Funds through the FCT - Fundação para a Ciência e a Tecnologia,I.P. (Portuguese Foundation for Science and Technology) within the project StorySense, withreference 2022.09312.PTDC (DOI 10.54499/2022.09312.PTDC).

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