Graph Embeddings for Abusive Language Detection

• Published in: SN computer science Vol. 2; no. 1; p. 37
• Main Authors: Cécillon, Noé, Labatut, Vincent, Dufour, Richard, Linarès, Georges
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.02.2021; Springer Nature B.V; Springer
• Subjects: Automation; Bullying; Classification; Computation and Language; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Feature selection; Graph representations; Graphical representations; Information Systems and Communication Service; Language; Messages; Methods; Neural networks; Original Research; Pattern Recognition and Graphics; Social and Information Networks; Social Media Analytics and its Evaluation; Social networks; Software Engineering/Programming and Operating Systems; Topology; Vision; Online conversations; Social networks; Graph embedding; Automatic abuse detection; Conversational graph
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-020-00413-7

Abusive behaviors are common on online social networks. The increasing frequency of anti-social behaviors forces the hosts of online platforms to find new solutions to address this problem. Automating the moderation process has thus received a lot of interest in the past few years. Various methods have been proposed, most based on the exchanged content, and one relying on the structure and dynamics of the conversation. It has the advantage of being language-independent, however it leverages a hand-crafted set of topological measures which are computationally expensive and not necessarily suitable to all situations. In the present paper, we propose to use recent graph embedding approaches to automatically learn representations of conversational graphs depicting message exchanges. We compare two categories: node vs. whole-graph embeddings. We experiment with a total of 8 approaches and apply them to a dataset of online messages. We also study more precisely which aspects of the graph structure are leveraged by each approach. Our study shows that the representation produced by certain embeddings captures the information conveyed by specific topological measures, but misses out other aspects.