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Antisocial Behavior Prediction: A Survey and Practical Guide

Paper · 2026 · Anaïs Ollagnier

Summary

Most research on harmful online behavior detects hate speech, harassment or trolling only after it has been posted. This survey addresses a less explored problem: anticipating antisocial behavior (ASB) before it happens. Examples include predicting whether a civil thread will derail, how hateful content will spread, or whether a user will reoffend.

The author searched Google Scholar, DBLP, Scopus and other databases and collected 49 machine-learning studies published since 2014. These are organized with a new taxonomy that describes tasks by two things: when harm is expected and the operational reason for modeling it. Studies are further divided by:

  • output type (classification vs. regression),
  • level (actors vs. content),
  • prediction timing (ex-ante vs. peeking).

The review links these task choices to datasets, models and evaluation practices, framed as a step-by-step guide for practitioners. It concludes that the field is fragmented. Benchmarks are not standardized, representations are mostly text-based, and accuracy often conflicts with interpretability. The paper serves as a shared reference for building earlier, fairer and more accountable moderation systems.

Key points

  • The review covers 49 ML studies since 2014. Output was about one paper per year in 2014–2017, grew modestly in 2018–2019, and rose sharply from 2020, with work appearing at EMNLP, NAACL, ICWSM and KDD.
  • ASB is defined across three harm dimensions: personal (harassment, cyberbullying), group-directed (identity-based hate) and environmental (trolling, misinformation, coordinated abuse).
  • The taxonomy has five task types: Harm Emergence, Harm Propagation, Early Harm Detection, Behavioral Risk and Proactive Moderation.
  • Ex-ante models predict using only information available at posting time. Peeking models first observe early replies, which usually improves accuracy but delays the prediction.
  • Open challenges include English-centric data, coarse labels that miss implicit hate, small biased datasets, concept drift, and ethical risks of user risk profiling. The author recommends human-in-the-loop oversight.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🔬Institut national de recherche en sciences et technologies du numériqueLe Chesnay-Rocquencourt · France85 works · 25,498 citations🔬Centre National de la Recherche ScientifiqueParis · France169 works · 62,488 citations🔬Laboratoire d'Informatique, Signaux et Systèmes de Sophia AntipolisValbonne · France3 works · 1 citation🎓Université Côte d'AzurNice · France3 works · 1 citation

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksSocial Sciences · 1,954 worksPsychology · 1,649 worksSociology and Political Science · 626 worksSocial Psychology · 330 works

Cite

Cite as Anaïs Ollagnier. 2026. Antisocial Behavior Prediction: A Survey and Practical Guide. The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.wassa-1.18

@inproceedings{ollagnier2026antisocial,
  title = {{Antisocial Behavior Prediction: A Survey and Practical Guide}},
  author = {Anaïs Ollagnier},
  booktitle = {The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.wassa-1.18},
  url = {https://doi.org/10.18653/v1/2026.wassa-1.18}
}

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