Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning

TL;DR

Addresses the challenge of antisocial behavior in multi-party online conversations by leveraging CyberAgressionAdo-Large, the largest publicly available French simulation dataset for ASB in group chats. The authors benchmark six lexical and eight graph-based representation learning methods and three fusion strategies to jointly model linguistic content and conversational structure across three tasks: ABD, BBA, and BPI. They find multimodal fusion consistently outperforms unimodal baselines, with late fusion mBERT + WD-SGCN achieving top ABD performance (0.718) and competitive BPI and BBA, while early fusion excels on BPI. The work underscores the value of integrating lexical cues with interaction dynamics to capture implicit aggression, role transitions, and peer-group dynamics, paving the way for more context-aware ASB detection in multi-party settings.

Abstract

Antisocial behavior (ASB) on social media -- including hate speech, harassment, and cyberbullying -- poses growing risks to platform safety and societal well-being. Prior research has focused largely on networks such as X and Reddit, while \textit{multi-party conversational settings} remain underexplored due to limited data. To address this gap, we use \textit{CyberAgressionAdo-Large}, a French open-access dataset simulating ASB in multi-party conversations, and evaluate three tasks: \textit{abuse detection}, \textit{bullying behavior analysis}, and \textit{bullying peer-group identification}. We benchmark six text-based and eight graph-based \textit{representation-learning methods}, analyzing lexical cues, interactional dynamics, and their multimodal fusion. Results show that multimodal models outperform unimodal baselines. The late fusion model \texttt{mBERT + WD-SGCN} achieves the best overall results, with top performance on abuse detection (0.718) and competitive scores on peer-group identification (0.286) and bullying analysis (0.606). Error analysis highlights its effectiveness in handling nuanced ASB phenomena such as implicit aggression, role transitions, and context-dependent hostility.

Figures (3)

• Figure 1: An example of a multi-party interaction, with participants and threads marked, extracted from the CyberAgressionAdo-Large dataset Ollagnier2024CyberAgressionAdo. Translation available in Appendix \ref{['sec:trans']}.
• Figure 2: Illustration of the three fusion strategies. Two independent embedding methods produce vector representations used by unimodal classifiers. The fusion phase introduces three classifiers---early, late, and hybrid---each integrating information differently (see main text).
• Figure 3: The translated version of Figure \ref{['fig:conv_fr']}.