research-article

You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights

Authors:

Animesh Mukherjee,

Binny MathewAuthors Info & Claims

HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media

Pages 79 - 89

https://doi.org/10.1145/3465336.3475106

Published: 29 August 2021 Publication History

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 other models including the fully supervised ones. We perform detailed error analysis on the best performing text and graph based models and observe that hateful users have unique network neighborhood signatures and the AGNN model benefits by paying attention to these signatures. This property, as we observe, also allows the model to generalize well across platforms in a zero-shot setting. Lastly, we utilize the best performing GNN model to analyze the evolution of hateful users and their targets over time in Gab.

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Cited By

Miao ZChen XWang HTang RYang ZHuang TTang W(2024)Detecting Offensive Language Based on Graph Attention Networks and Fusion FeaturesIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325050211:1(1493-1505)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3250502
Shome DKar T(2021)ConOffense: Multi-modal multitask Contrastive learning for offensive content identification2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671427(4524-4529)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671427

Index Terms

You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing

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cover image ACM Conferences

HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media

August 2021

306 pages

ISBN:9781450385510

DOI:10.1145/3465336

General Chair:
Owen Conlan
Trinity College Dublin, Ireland
,
Program Chair:
Eelco Herder
Radboud Universiteit Nijmegen, Netherlands

Copyright © 2021 ACM.

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Published: 29 August 2021

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Cited By

Miao ZChen XWang HTang RYang ZHuang TTang W(2024)Detecting Offensive Language Based on Graph Attention Networks and Fusion FeaturesIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325050211:1(1493-1505)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3250502
Shome DKar T(2021)ConOffense: Multi-modal multitask Contrastive learning for offensive content identification2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671427(4524-4529)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671427

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