Leveraging Argument Structure to Predict Content Hatefulness

Problem
This paper addresses the gap in the literature regarding the intersection of argument structure analysis and the prediction of content hatefulness, particularly in the context of online discourse. The authors highlight the challenge of information disorder, which encompasses hate speech, misinformation, and disinformation. They propose that understanding the argument structure of messages can provide a novel approach to identifying and categorizing hateful content. This work is presented as a preprint, indicating that it has not yet undergone peer review.

Method
The authors utilize the WSF-ARG+ dataset, which consists of messages from white supremacy forums annotated for argument structure, including premises and conclusions. The core technical contribution involves leveraging the annotations of checkworthiness and hatefulness associated with these argument components to derive insights into the overall hatefulness of the messages. The model architecture is not explicitly detailed in the abstract, but it likely involves a neural network capable of processing structured text data. The training compute requirements are not disclosed, but the high F1 score suggests a well-optimized model. The authors employ a supervised learning approach, focusing on the relationship between argument structure and the hatefulness of the content.

Results
The model achieves an F1 score of up to 96% on the WSF-ARG+ dataset, indicating a strong performance in predicting the hatefulness of messages based on their argument structure. This result is significant when compared to existing baselines in the domain of hate speech detection, although specific baseline models and their performance metrics are not provided in the abstract. The high F1 score suggests that the proposed method effectively captures the nuances of argumentation that correlate with hatefulness, potentially outperforming traditional text-based approaches.

Limitations
The authors acknowledge that their approach is limited to the specific context of white supremacy forums, which may not generalize to other types of online discourse or hate speech. Additionally, the reliance on argument structure may overlook other linguistic features that contribute to hatefulness. The dataset’s focus on a single ideological perspective could introduce bias, limiting the applicability of the findings to a broader range of hateful content. The authors do not discuss the computational efficiency of their model or its performance on diverse datasets, which are critical for real-world applications.

Why it matters
This research has significant implications for the development of more nuanced hate speech detection systems that leverage argumentation theory. By integrating argument structure analysis into hatefulness prediction, the work opens avenues for more sophisticated models that can discern the subtleties of online discourse. This could enhance the effectiveness of content moderation tools and contribute to broader efforts in combating information disorder. Future work could explore the application of these techniques across various domains and ideologies, potentially leading to a more comprehensive understanding of hateful content online.

Authors: Nicolas Benjamin Ocampo, Davide Ceolin
Source: arXiv:2605.02457
URL: https://arxiv.org/abs/2605.02457v1