2020/04/18 by Autumn Toney, Toney, Autumn, Akshat Pandey +7
Computer Science · Social Sciences · #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Machine Learning (cs.LG) #Misinformation and Its Impacts #Social Media and Politics #cs.CL #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.2004.08726
5 pages, 4 tables, 1 figure
openalex publication_date 2020/04/18 · arxiv created 2020/12/04 · arxiv updated 2020/12/07 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
This paper considers the problem of automatically characterizing overall attitudes and biases that may be associated with emerging information operations via artificial intelligence. Accurate analysis of these emerging topics usually requires laborious, manual analysis by experts to annotate millions of tweets to identify biases in new topics. We introduce extensions of the Word Embedding Association Test from Caliskan et al. to a new domain (Caliskan, 2017). Our practical and unsupervised method is used to quantify biases promoted in information operations. We validate our method using known information operation-related tweets from Twitter's Transparency Report. We perform a case study on the COVID-19 pandemic to evaluate our method's performance on non-labeled Twitter data, demonstrating its usability in emerging domains.