Topics and emotions in Russian Twitter propaganda

Abstract

The increasing importance of social media to political communication means the study of government-sponsored social media activity deserves further exploration. In particular, text-as-data techniques like topic models and emotional lexicons provide potential for new types of content analysis of large collections of government-backed social media discourse. Applying text-as-data methods to a corpus of Russian-sponsored Twitter data generated before, during and after the 2016 U.S. presidential election shows tweets containing a diverse set of policy-related topics as well as levels of angry and fearful emotional language that peaks in close association to the election. Text-as-data techniques show Russian sponsored tweets mentioned candidate Clinton overwhelmingly negatively and referenced candidate Trump in a positive but less consistent manner. The tweets contained large minorities of apolitical topics, and also saw higher levels of conservative hashtags than progressive ones. Topics within the tweet data show a contradictory set of topics on all “sides” of the political spectrum alongside increases in fearful and angry language in temporal association with the U.S. election. The findings of this inquiry provide evidence that the tweets were sent to heighten existing tensions through topically heterogeneous propaganda. They also caution against an overly black and white interpretation of Russian disinformation efforts online.