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.

翻译：社交媒体上反社会行为的再度兴起，加剧了对刻板观念的负面强化、针对个人与社会群体的仇恨言论，以及虚假或扭曲新闻的传播。图神经网络在处理大规模图结构数据方面的进展，为社交媒体平台上沟通调解的未来带来了巨大希望。我们采用了一种基于图卷积数据的方法，以更好地捕捉异构数据类型之间的依赖关系。基于对该主题的过往及现有经验，我们提出并评估了一种基于图的反社会行为检测方法，该方法具有广泛适用性，且与语言和上下文无关。在本研究中，我们利用PAN共享任务提供的多个PAN数据集，对所提出的基于图的方法进行了实验验证，从而能够讨论该解决方案所取得的结果。