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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 > No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

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

February 1, 2023

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


Proceedings:

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 35

Track:

AAAI Special Track on AI for Social Impact

Downloads:

Download PDF

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.

DOI:

10.1609/aaai.v35i17.17738


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 35



Topics: AAAI

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