Countering Online Hate Speech: An NLP Perspective

Chandni Saxena; Helen Meng; Mudit Chaudhary

Countering Online Hate Speech: An NLP Perspective

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arxiv 2109.02941 v1 pith:X75MBPGC submitted 2021-09-07 cs.CL cs.LG

Mudit Chaudhary , Chandni Saxena , Helen Meng This is my paper · ORCID

classification cs.CLcs.LG

keywords onlinehatespeechcounteringhate-speechresearchactionalong

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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.

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Understanding and Analyzing Inappropriately Targeting Language in Online Discourse: A Comparative Annotation Study
cs.CL 2025-05 conditional novelty 4.0 of 10

A comparative annotation study finds ChatGPT over-identifies inappropriate targeting in Reddit conversations and uncovers four new target categories beyond the standard hate speech classes.

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