Countering Online Hate Speech: An NLP Perspective
This work addresses the harmful impact of online hate speech on social media users, but it is incremental as it primarily surveys and organizes existing research rather than introducing new methods.
The paper tackles the problem of online hate speech by presenting a conceptual framework and survey of NLP methods for countering it, focusing on prevention and intervention rather than just identification.
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.