2019/05/31 by Bo Wang, Wang, Bo, Baixiang Xue +3
Computer Science · Social Sciences · #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Social and Intergroup Psychology #cs.CL #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.1905.13364
arxiv created 2019/05/31 · openalex publication_date 2019/05/31 · arxiv updated 2019/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Language is a popular resource to mine speakers' attitude bias, supposing that speakers' statements represent their bias on concepts. However, psychology studies show that people's explicit bias in statements can be different from their implicit bias in mind. Although both explicit and implicit bias are useful for different applications, current automatic techniques do not distinguish them. Inspired by psychological measurements of explicit and implicit bias, we develop an automatic language-based technique to reproduce psychological measurements on large population. By connecting each psychological measurement with the statements containing the certain combination of special words, we derive explicit and implicit bias by understanding the sentiment of corresponding category of statements. Extensive experiments on English and Chinese serious media (Wikipedia) and non-serious media (social media) show that our method successfully reproduce the small-scale psychological observations on large population and achieve new findings.