Political Hate Speech Detection and Lexicon Building: A Study in Taiwan

IEEE Access

Chun-Lian Wu¹  Chih-Chien Wang¹  Min-Yuh Day¹ 

[1] Graduate Institute of Information Management, National Taipei University, New Taipei City, Taiwan;

关键词: BERT;    bidirectional encoder representations from transformers;    deep learning;    hate speech;    lexicon;    N-gram;   

DOI  :  10.1109/ACCESS.2022.3160712

来源: DOAJ

【 摘 要 】

There is the minimal restriction to users’ speech in cyberspace. The Internet provides a space where people can freely present their speech, which puts a Utopian sense of freedom of speech into practice. However, the appearance of hate speech is a significant side effect of online freedom of speech. Some users use hate speech to attack others, making the attacked targets uncomfortable. The proliferation of hate speech poses severe challenges to cyber society. Users may hope that social media platforms and online communities promote anti-hate speech. However, hate speech detection is still a developing technology that requires system developers to create a method to detect unacceptable hate speech while maintaining the online freedom of speech environment. No excellence detection approach has yet been proposed, although some literature has focused on it. The current study proposes an approach to build a political hate speech lexicon and train artificial intelligence classifiers to detect hate speech. Our academic and practical contributions include the collection of a Chinese hate speech dataset, creating a Chinese hate speech lexicon, and developing both a deep learning-based and a lexicon-based approach to detect Chinese hate speech. Although we focus on Chinese hate speech detection, our proposed hate speech detection system and hate speech lexicon development approach can also be used for other languages.

【 授权许可】

Unknown