No abstract

Toxic language is often present in online forums, especially when politics and other polarizing topics arise, and can lead to people becoming discouraged from joining or continuing conversations. In this paper, I use data consisting of comments with the indices of toxic text labelled to train an RNN to determine which parts of the comments make them toxic, which could aid online moderators. I compare results using both the original dataset and an augmented set, as well as GRU versus LSTM RNN models.