Contextual-Lexicon Approach for Abusive Language Detection
This addresses the problem of detecting offensive content in Portuguese social media, but it is incremental as it adapts lexicon-based methods with contextual annotations.
The paper tackles abusive language detection in social media by introducing a contextual-lexicon approach, which outperforms baseline methods for Portuguese.
Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media. Our approach embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.