Detecting the Hate Code on Social Media

Author:

Magu Rijul,Joshi Kshitij,Luo Jiebo

Abstract

Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google’s Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets. For example, users have used the words Googles and Bings to represent the African-American and Asian communities, respectively. By generating the list of users who post such content, we move a step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Twitter Hate Speech Detection using Machine Learning;2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN);2024-05-03

2. A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions;PeerJ Computer Science;2024-04-02

3. Detecting Offensive Language Based on Graph Attention Networks and Fusion Features;IEEE Transactions on Computational Social Systems;2024-02

4. Hate Speech Detection and Bias in Supervised Text Classification;The Oxford Handbook of the Sociology of Machine Learning;2023-12-18

5. The Impact of Data Pre-Processing on Hate Speech Detection in a Mix of English and Hindi–English (Code-Mixed) Tweets;Applied Sciences;2023-10-09

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