Improving Abusive Language Detection with online interaction network

Rui Song, Fausto Giunchiglia, Qiang Shen, Nan Li, Hao Xu

Abstract: Highlights•We consider the rationality of introducing community structural information into Abusive Language Detection task, and propose a pipeline framework to integrate contextual, semantic, and community structural features contained in tweets.•We build relation-special graphs for tweets as well as comments and design a Relation-Special Graph Neural Network to learn useful information from the graphs.•We formalize the validity of the proposed framework through experiments and verify that our method can bring greater performance improvement in the case of community density and less training data.