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Computational Approaches for Cyber Social Threats

EPJ Data Science is delighted to present a topical issue on Computational Approaches for Cyber Social Threats

This topical issue aims to bring together innovative research contributions that leverage computational approaches to tackle cyber social threats. Cyber social threats are increasingly prevalent in our digitally interconnected society and include fake news, disinformation campaigns, cyberbullying, hate speech, and online radicalization. These threats have significant societal consequences, including the erosion of trust in institutions, polarized public discourse, and the exacerbation of societal divides.

We include the spotlight topic Information Integrity During Crises, chosen in light of recent global events that have underscored the importance of reliable information. These crises provide fertile ground for the spread of disinformation and misinformation, making it challenging to separate fact from fiction. Research focused on this topic includes state-of-the-art approaches to combat the spread of misinformation and to promote accurate and timely communication. The goal of this topical issue is to advance our understanding of how computational methods can be harnessed to address cyber social threats and to promote the integrity of information during crises.

Guest Editors:

Francesco Pierri, Politecnico di Milano, Milano, Italy
Matthew R. DeVerna, Indiana University, Bloomington, IN, USA  
Kai-Cheng Yang, Indiana University, Bloomington, IN, USA   
Jeremy Blackburn, State University of New York at Binghamton, NY, USA
Ugur Kursuncu, Georgia State University, GA, USA

Submission deadline: Closed for Submissions.


  1. This paper examines Russia’s propaganda discourse on Twitter during the 2022 invasion of Ukraine. The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to i...

    Authors: Iuliia Alieva, Ian Kloo and Kathleen M. Carley
    Citation: EPJ Data Science 2024 13:42
  2. In an era of increasing political polarization, its analysis becomes crucial for the understanding of democratic dynamics. This paper presents a comprehensive research on measuring political polarization on X ...

    Authors: Pau Muñoz, Alejandro Bellogín, Raúl Barba-Rojas and Fernando Díez
    Citation: EPJ Data Science 2024 13:39
  3. Organized attempts to manipulate public opinion during election run-ups have dominated online debates in the last few years. Such attempts require numerous accounts to act in coordination to exert influence. Yet,...

    Authors: Serena Tardelli, Leonardo Nizzoli, Marco Avvenuti, Stefano Cresci and Maurizio Tesconi
    Citation: EPJ Data Science 2024 13:33
  4. For U.S. presidential elections, most states use the so-called winner-take-all system, in which the state’s presidential electors are awarded to the winning political party in the state after a popular vote ph...

    Authors: Manuel Pratelli, Marinella Petrocchi, Fabio Saracco and Rocco De Nicola
    Citation: EPJ Data Science 2024 13:25
  5. The same individuals can express very different emotions in online social media with respect to face-to-face interactions, partially because of intrinsic limitations of the digital environments and partially b...

    Authors: Anna Bertani, Riccardo Gallotti, Stefano Menini, Pierluigi Sacco and Manlio De Domenico
    Citation: EPJ Data Science 2024 13:20
  6. The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility acc...

    Authors: Bao Tran Truong, Oliver Melbourne Allen and Filippo Menczer
    Citation: EPJ Data Science 2024 13:10
  7. It is often thought that an external threat increases the internal cohesion of a nation, and thus decreases polarization. We examine this proposition by analyzing NATO discussion dynamics on Finnish social med...

    Authors: Yan Xia, Antti Gronow, Arttu Malkamäki, Tuomas Ylä-Anttila, Barbara Keller and Mikko Kivelä
    Citation: EPJ Data Science 2024 13:1