A Semi-Supervised Approach to Detect Toxic Comments
Ghivvago D. Saraiva, Rafael T. Anchiêta, Francisco Assis Ricarte Neto, Raimundo Santos Moura · 2021
Toxic comments contain forms of nonacceptable language targeted towards groups or individuals.These types of comments become a serious concern for government organizations, online communities, and social media platforms.Although there are some approaches to handle non-acceptable language, most of them focus on supervised learning and the English language.In this paper, we deal with toxic comment detection as a semi-supervised strategy over a heterogeneous graph.We evaluate the approach on a toxic dataset of the Portuguese language, outperforming several graph-based methods and achieving competitive results compared to transformer architectures.