Publications

  1. Tessa, B., Cima, L., Trujillo, A., Avvenuti, M., & Cresci, S. (2025). Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention. Engineering Applications of Artificial Intelligence, 162, 112375. https://doi.org/10.1016/j.engappai.2025.112375
    link arXiv
  2. Shahi, G. K., Tessa, B., Trujillo, A., & Cresci, S. (2025). A Year of the DSA Transparency Database: What it (Does Not) Reveal About Platform Moderation During the 2024 European Parliament Election. In Proceedings of the Workshop COMPASS 2025: The 1st International Workshop on Computational Approaches to Content Moderation and Platform Governance. https://workshop-proceedings.icwsm.org/abstract.php?id=2025_08
    link arXiv
  3. Tessa, B., Amram, D., Monreale, A., & Cresci, S. (2025). Improving regulatory oversight in online content moderation. ArXiv Preprint ArXiv:2506.04145.
    arXiv
  4. Trujillo, A., Fagni, T., & Cresci, S. (2025). The DSA Transparency Database: Auditing self-reported moderation actions by social media. Proceedings of the ACM on Human-Computer Interaction, 9(2), 1–28. https://dl.acm.org/doi/abs/10.1145/3711085
    link arXiv
  5. Cima, L., Tessa, B., Trujillo, A., Cresci, S., & Avvenuti, M. (2025). Investigating the heterogeneous effects of a massive content moderation intervention via Difference-in-Differences. Online Social Networks and Media, 48, 100320. https://doi.org/10.1016/j.osnem.2025.100320
    link arXiv
  6. Tessa, B., Moreo, A., Cresci, S., Fagni, T., & Sebastiani, F. (2025). Quantifying Feature Importance for Online Content Moderation. ArXiv Preprint ArXiv:2510.19882.
    arXiv
  7. Cerulli, A., Cima, L., Tessa, B., Tardelli, S., & Cresci, S. (2026). The Big Ban Theory: A Pre-and Post-Intervention Dataset of Online Content Moderation Actions. ArXiv Preprint ArXiv:2601.11128.
    arXiv
  8. Cerulli, A., Tessa, B., La Selva, G., Mazzeo, O., Cima, L., Monacis, L., & Cresci, S. (2025). Dark Personality Traits and Online Toxicity: Linking Self-Reports to Reddit Activity. ArXiv Preprint ArXiv:2512.10113.
    arXiv
  9. Cima, L., Miaschi, A., Trujillo, A., Avvenuti, M., Dell’Orletta, F., & Cresci, S. (2025). Contextualized counterspeech: Strategies for adaptation, personalization, and evaluation. Proceedings of the ACM on Web Conference 2025, 5022–5033. https://dl.acm.org/doi/pdf/10.1145/3696410.3714507
    link arXiv
  10. Cima, L., Trujillo, A., Avvenuti, M., & Cresci, S. (2024). The Great Ban: Efficacy and Unintended Consequences of a Massive Deplatforming Operation on Reddit. Companion Publication of the 16th ACM Web Science Conference, 85–93. https://dl.acm.org/doi/abs/10.1145/3630744.3663608
    link arXiv
  11. Giorgi, T., Cima, L., Fagni, T., Avvenuti, M., & Cresci, S. (2025). Human and LLM biases in hate speech annotations: A socio-demographic analysis of annotators and targets. Proceedings of the International AAAI Conference on Web and Social Media, 19, 653–670. https://ojs.aaai.org/index.php/ICWSM/article/download/35837/37991
    link arXiv
  12. Mannocci, L., Cresci, S., Magnani, M., Monreale, A., & Tesconi, M. (2026). Multimodal coordinated online behavior: Trade-offs and strategies. Information Sciences, 123125. https://doi.org/10.1016/j.ins.2026.123125
    link arXiv
  13. Cascione, A., Setzu, M., Galatolo, F. A., Cimino, M. G. C. A., & Guidotti, R. (2024). Interpretable Machine Learning for Oral Lesion Diagnosis Through Prototypical Instances Identification. International Conference on Discovery Science, 316–331. https://doi.org/10.1007/978-3-031-78980-9_20
    link arXiv
  14. Cinquini, M., Makhlouf, K., Zhioua, S., Palamidessi, C., & Guidotti, R. (2025). A Bias Injection Technique to Assess the Resilience of Causal Discovery Methods. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3573201
    link
  15. Landi, C., Cascione, A., Manerba, M. M., & Guidotti, R. (2025). Balancing Fairness and Interpretability in Clustering with FairParTree. World Conference on Explainable Artificial Intelligence, 104–127. https://doi.org/10.1007/978-3-032-08324-1_5
    link
  16. Pollacci, L., Gneri, J., & Guidotti, R. (2025). An Interpretable Data-Driven Approach for Modeling Toxic Users via Feature Extraction. World Conference on Explainable Artificial Intelligence, 180–201. https://doi.org/10.1007/978-3-032-08327-2_9
    link
  17. Cascione, A., Pollacci, L., & Guidotti, R. (2025). Unsupervised and Interpretable Detection of User Personalities in Online Social Networks. World Conference on Explainable Artificial Intelligence, 162–179. https://doi.org/10.1007/978-3-032-08327-2_8
    link
  18. Corbucci, L., Legrottaglie, J. A. B., Spinnato, F., Monreale, A., & Guidotti, R. (2025). An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items. In Proceedings of the 28th European Conference on Artificial Intelligence (ECAI 2025). https://dx.doi.org/10.3233/faia250912
    link arXiv
  19. Coda-Giorgio, L., Fidone, G., & Pollacci, L. (2025). Multi-domain Validation of LLM-Based Simulators via Interpretable and Latent Representations. International Conference on Discovery Science, 411–426. https://doi.org/10.1007/978-3-032-05461-6_27
    link
  20. Milli, L., Pollacci, L., & Guidotti, R. (2025). Evaluating Moderation in Online Social Network. ArXiv Preprint ArXiv:2512.20225.
    arXiv
  21. Fidone, G., Passaro, L., & Guidotti, R. (2025). Evaluating Online Moderation Via LLM-Powered Counterfactual Simulations. ArXiv Preprint ArXiv:2511.07204.
    arXiv
  22. Cascione, A., Cerulli, A., Manerba, M. M., & Passaro, L. (2024). Women’s Professions and Targeted Misogyny Online. Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-It 2024), 182–189. https://aclanthology.org/2024.clicit-1.22.pdf
    link
  23. Cascione, A., Cerulli, A., Manerba, M. M., & Passaro, L. (2024). Investigating the Hurtfulness of Misogynistic Tweets Across Professions. Proceedings of the Discovery Science Late Breaking Contributions 2024 (DS-LB 2024) Co-Located with 27th International Conference Discovery Science 2024. https://ceur-ws.org/Vol-3928/paper_174.pdf
    link