Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter
This addresses the need for efficient, unsupervised analysis of information operations for researchers and platforms, though it is incremental as it adapts an existing method to a new domain.
The paper tackles the problem of automatically analyzing biases in information operations on Twitter by extending the Word Embedding Association Test to this domain, validating it with known data and demonstrating its application to COVID-19 discourse.
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.