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Graph Neural Networks for Antisocial Behavior Detection on Twitter

arXiv 2312.16755 ↗First posted 28 Dec 2023cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 28 Dec 2023.

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References · 14

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2 more
  1. 2020VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classificationcited by 35
  2. 2021Cross-lingual Text Classification with Heterogeneous Graph Neural Networkcited by 12

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