Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior

TL;DR

This paper systematically reviews predictive methods for online antisocial behavior, categorizing tasks, analyzing modeling trends, and highlighting challenges to guide future research in proactive moderation.

Contribution

It provides the first comprehensive taxonomy of ASB prediction tasks and synthesizes existing methods, datasets, and challenges in a unified framework.

Findings

Identifies five core ASB prediction task types.

Highlights methodological challenges like dataset scarcity and temporal drift.

Outlines future directions including multilingual and cross-platform models.

Abstract

Antisocial behavior (ASB) on social media-including hate speech, harassment, and trolling-poses growing challenges for platform safety and societal wellbeing. While prior work has primarily focused on detecting harmful content after it appears, predictive approaches aim to forecast future harmful behaviors-such as hate speech propagation, conversation derailment, or user recidivism-before they fully unfold. Despite increasing interest, the field remains fragmented, lacking a unified taxonomy or clear synthesis of existing methods. This paper presents a systematic review of over 49 studies on ASB prediction, offering a structured taxonomy of five core task types: early harm detection, harm emergence prediction, harm propagation prediction, behavioral risk prediction, and proactive moderation support. We analyze how these tasks differ by temporal framing, prediction granularity, and operational goals. In addition, we examine trends in modeling techniques-from classical machine learning to pre-trained language models-and assess the influence of dataset characteristics on task feasibility and generalization. Our review highlights methodological challenges, such as dataset scarcity, temporal drift, and limited benchmarks, while outlining emerging research directions including multilingual modeling, cross-platform generalization, and human-in-the-loop systems. By organizing the field around a coherent framework, this survey aims to guide future work toward more robust and socially responsible ASB prediction.