You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights
Mithun Das, Punyajoy Saha, Ritam Dutt, Pawan Goyal, Animesh Mukherjee, and Binny Mathew

TL;DR
This paper explores the detection of hateful users on social media by combining textual and social network features using Graph Neural Networks, achieving high accuracy and generalization across platforms.
Contribution
It introduces a semi-supervised GNN approach that leverages both text and social connections, outperforming traditional models in hate speech user detection.
Findings
AGNN achieves 0.791 macro F1 on Gab dataset
Model generalizes well across platforms in zero-shot setting
Hateful users have distinctive network neighborhood signatures
Abstract
Hate speech is regarded as one of the crucial issues plaguing the online social media. The current literature on hate speech detection leverages primarily the textual content to find hateful posts and subsequently identify hateful users. However, this methodology disregards the social connections between users. In this paper, we run a detailed exploration of the problem space and investigate an array of models ranging from purely textual to graph based to finally semi-supervised techniques using Graph Neural Networks (GNN) that utilize both textual and graph-based features. We run exhaustive experiments on two datasets -- Gab, which is loosely moderated and Twitter, which is strictly moderated. Overall the AGNN model achieves 0.791 macro F1-score on the Gab dataset and 0.780 macro F1-score on the Twitter dataset using only 5% of the labeled instances, considerably outperforming all the…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Social Media and Politics · Internet Traffic Analysis and Secure E-voting
