  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.
