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arXiv:2109.02941 (cs)
COVID-19 e-print

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[Submitted on 7 Sep 2021]

Title:Countering Online Hate Speech: An NLP Perspective

Authors:Mudit Chaudhary, Chandni Saxena, Helen Meng
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Abstract:Online hate speech has caught everyone's attention from the news related to the COVID-19 pandemic, US elections, and worldwide protests. Online toxicity - an umbrella term for online hateful behavior, manifests itself in forms such as online hate speech. Hate speech is a deliberate attack directed towards an individual or a group motivated by the targeted entity's identity or opinions. The rising mass communication through social media further exacerbates the harmful consequences of online hate speech. While there has been significant research on hate-speech identification using Natural Language Processing (NLP), the work on utilizing NLP for prevention and intervention of online hate speech lacks relatively. This paper presents a holistic conceptual framework on hate-speech NLP countering methods along with a thorough survey on the current progress of NLP for countering online hate speech. It classifies the countering techniques based on their time of action, and identifies potential future research areas on this topic.
Comments: 12 pages
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2109.02941 [cs.CL]
  (or arXiv:2109.02941v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2109.02941

Submission history

From: Chandni Saxena [view email]
[v1] Tue, 7 Sep 2021 08:48:13 UTC (563 KB)
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