Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention

Authors

  • Hongyu Gong University of Illinois at Urbana-Champaign
  • Alberto Valido University of North Carolina at Chapel Hill
  • Katherine M. Ingram University of North Carolina at Chapel Hill
  • Giulia Fanti Carnegie Mellon University
  • Suma Bhat University of Illinois at Urbana-Champaign
  • Dorothy L. Espelage University of North Carolina at Chapel Hill

DOI:

https://doi.org/10.1609/aaai.v35i17.17738

Keywords:

Web, Philosophical and Ethical Issues, Computational Social Science

Abstract

Abusive language is a massive problem in online social platforms. Existing abusive language detection techniques are particularly ill-suited to comments containing heterogeneous abusive language patterns, i.e., both abusive and non-abusive parts. This is due in part to the lack of datasets that explicitly annotate heterogeneity in abusive language. We tackle this challenge by providing an annotated dataset of abusive language in over 11,000 comments from YouTube. We account for heterogeneity in this dataset by separately annotating both the comment as a whole and the individual sentences that comprise each comment. We then propose an algorithm that uses a supervised attention mechanism to detect and categorize abusive content using multi-task learning. We empirically demonstrate the challenges of using traditional techniques on heterogeneous content and the comparative gains in performance of the proposed approach over state-of-the-art methods.

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Published

2021-05-18

How to Cite

Gong, H., Valido, A., Ingram, K. M., Fanti, G., Bhat, S., & Espelage, D. L. (2021). Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14804-14812. https://doi.org/10.1609/aaai.v35i17.17738

Issue

Section

AAAI Special Track on AI for Social Impact