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    <abstract>Antisocial behavior (ASB) on social media encompasses online behaviors that harm individuals, groups, or platform ecosystems, including hate speech, harassment, cyberbullying, trolling, and coordinated abuse. While most prior work has focused on detecting harm after it occurs, a growing body of research on ASB prediction seeks to forecast future harmful outcomes before they materialize, including—but not limited to—hate-speech diffusion, conversational derailment, and user recidivism. However, this emerging field remains fragmented, with limited conceptual grounding and few integrative frameworks. This paper establishes a foundation for ASB prediction by introducing a structured taxonomy spanning temporal, structural, and behavioral dimensions. Drawing on 49 machine learning studies identified through a literature review, we map predictive goals to datasets, modeling choices, and evaluation practices, and identify key challenges, including the lack of standardized benchmarks, the dominance of text-centric representations, and trade-offs between accuracy and interpretability. We conclude by outlining actionable directions toward more robust, generalizable, and responsible ASB prediction systems.</abstract>
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%0 Conference Proceedings
%T Antisocial Behavior Prediction: A Survey and Practical Guide
%A Ollagnier, Anaïs
%Y Barnes, Jeremy
%Y Barriere, Valentin
%Y De Clercq, Orphée
%Y Klinger, Roman
%Y Nouri, Célia
%Y Nozza, Debora
%Y Singh, Pranaydeep
%S The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-378-4
%F ollagnier-2026-antisocial
%X Antisocial behavior (ASB) on social media encompasses online behaviors that harm individuals, groups, or platform ecosystems, including hate speech, harassment, cyberbullying, trolling, and coordinated abuse. While most prior work has focused on detecting harm after it occurs, a growing body of research on ASB prediction seeks to forecast future harmful outcomes before they materialize, including—but not limited to—hate-speech diffusion, conversational derailment, and user recidivism. However, this emerging field remains fragmented, with limited conceptual grounding and few integrative frameworks. This paper establishes a foundation for ASB prediction by introducing a structured taxonomy spanning temporal, structural, and behavioral dimensions. Drawing on 49 machine learning studies identified through a literature review, we map predictive goals to datasets, modeling choices, and evaluation practices, and identify key challenges, including the lack of standardized benchmarks, the dominance of text-centric representations, and trade-offs between accuracy and interpretability. We conclude by outlining actionable directions toward more robust, generalizable, and responsible ASB prediction systems.
%U https://aclanthology.org/2026.wassa-1.18/
%P 235-251

Markdown (Informal)

[Antisocial Behavior Prediction: A Survey and Practical Guide](https://aclanthology.org/2026.wassa-1.18/) (Ollagnier, WASSA 2026)

• Antisocial Behavior Prediction: A Survey and Practical Guide (Ollagnier, WASSA 2026)

ACL

• Anaïs Ollagnier. 2026. Antisocial Behavior Prediction: A Survey and Practical Guide. In The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026), pages 235–251, Rabat, Morocco. Association for Computational Linguistics.