Leveraging Argument Structure to Predict Content Hatefulness

arXiv cs.CL / 5/5/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

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Key Points

The paper investigates how argument structure (premises and conclusions) can help predict the overall hatefulness of online content involved in information disorder.
It uses the WSF-ARG+ dataset, which contains annotated messages from white supremacy forums with argument-structure labels.
The study leverages checkworthiness and hatefulness annotations of individual argument components to infer hatefulness at the full-message level.
Results are reported as promising, reaching up to 96% F1, suggesting this approach could be extended for hate-speech detection and countering information disorder.
The authors propose that linking different facets of the information disorder problem via shared argument-structure signals may enable more comprehensive solutions.

Abstract

Information disorder is a challenging phenomenon that affects society at large. This phenomenon entails the diffusion of misleading, misinforming, and hateful content online. In different contexts, one aspect of the problem may prevail, but overall, this is a broad problem that requires comprehensive solutions. While each dimension of the problem (hate speech, disinformation, misinformation, etc.) requires in-depth analysis, in this paper, we look into the possibility of argument structure to provide relevant information to link these different areas of the problem. In particular, we focus on the WSF-ARG+ dataset, which consists of white supremacy forum messages annotated in terms of argument structure (premises and conclusion). There, we leverage the checkworthiness and hatefulness annotations of the argument components to obtain insights into the hatefulness of the whole message. Our results show promising insights (up to 96% F1), indicating the possibility of extending this direction in the future to tackle hateful content identification and information disorder countering.