Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings Dorottya Demszky1 Nikhil Garg1 Rob Voigt1 James Zou1 Matthew Gentzkow1 Jesse Shapiro2 Dan Jurafsky1 1Stanford University 2Brown University fddemszky, nkgarg, robvoigt, jamesz, gentzkow,
[email protected] jesse shapiro
[email protected] Abstract 2016) and Facebook (Bakshy et al., 2015). Prior NLP work has shown, e.g., that polarized mes- We provide an NLP framework to uncover sages are more likely to be shared (Zafar et al., four linguistic dimensions of political polar- 2016) and that certain topics are more polarizing ization in social media: topic choice, framing, affect and illocutionary force. We quantify (Balasubramanyan et al., 2012); however, we lack these aspects with existing lexical methods, a more broad understanding of the many ways that and propose clustering of tweet embeddings polarization can be instantiated linguistically. as a means to identify salient topics for analy- This work builds a more comprehensive frame- sis across events; human evaluations show that work for studying linguistic aspects of polariza- our approach generates more cohesive topics tion in social media, by looking at topic choice, than traditional LDA-based models. We ap- framing, affect, and illocutionary force. ply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the 1.1 Mass Shootings discussion of these events is highly polarized politically and that this polarization is primar- We explore these aspects of polarization by study- ily driven by partisan differences in framing ing a sample of more than 4.4M tweets about rather than topic choice.