Attending the Emotions to Detect Online Abusive Language
Niloofar Safi Samghabadi, Afsheen Hatami, Mahsa Shafaei, Sudipta Kar, Thamar Solorio · 2020
In recent years, abusive behavior has become a serious issue in online social networks.In this paper, we present a new corpus for the task of abusive language detection that is collected from a semi-anonymous online platform, and unlike the majority of other available resources, is not created based on a specific list of bad words.We also develop computational models to incorporate emotions into textual cues to improve aggression identification.We evaluate our proposed methods on a set of corpora related to the task and show promising results with respect to abusive language detection.