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Effectiveness of Supervised Classification Models for Hate Speech on Twitter

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posted on 29.10.2020, 16:04 by Kunal Srivastava, Ryan Tabrizi, Ayaan Rahim, Lauryn Nakamitsu

Abstract

The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment.

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Email Address of Submitting Author

kunal@masagroup.com

Submitting Author's Institution

Bellevue High School

Submitting Author's Country

United States of America

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