Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter

This paper considers the problem of automatically characterizing 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 adaptations of the Word Embedding Association Test [1] to a new domain: information operations. We validate our method using known information operation-related tweets from Twitter's Transparency Reports, and 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.