Graph Neural Networks for Antisocial Behavior Detection on Twitter
This addresses the problem of detecting harmful content like hate speech and misinformation on social media platforms, though it appears incremental as it applies existing graph neural network methods to this domain.
The researchers tackled antisocial behavior detection on Twitter by developing a graph-based approach using graph convolutional networks to capture dependencies in heterogeneous data, achieving experimental validation on PAN datasets.
Social media resurgence of antisocial behavior has exerted a downward spiral on stereotypical beliefs, and hateful comments towards individuals and social groups, as well as false or distorted news. The advances in graph neural networks employed on massive quantities of graph-structured data raise high hopes for the future of mediating communication on social media platforms. An approach based on graph convolutional data was employed to better capture the dependencies between the heterogeneous types of data. Utilizing past and present experiences on the topic, we proposed and evaluated a graph-based approach for antisocial behavior detection, with general applicability that is both language- and context-independent. In this research, we carried out an experimental validation of our graph-based approach on several PAN datasets provided as part of their shared tasks, that enable the discussion of the results obtained by the proposed solution.