In their new article, “Talking Past Each Other on Twitter: Thematic, Event, and Temporal Divergences in Polarized Partisan Expression on Immigration,” published in Political Communication, the SMAD team studied patterns of polarized expression from opposing camps on social media and found that polarized partisan expression can be categorized into three groups: thematic emphasis, real-world event response and temporal disconnect. These categories not only explain existing concepts about polarization but speak to the deep partisan divide.
Extending literature on political polarization and political expression, we study patterns of polarized expression by vocal partisans from opposing camps on social media. Specifically, we argue that polarized partisan expression can be characterized by three divergences: 1) different thematic emphases on the same issue; 2) response to different real-world events on the same issue; and 3) a temporal disconnect at the aggregate level. Highlighting how online expression by different partisan groups is animated by discrete concerns and events and exhibits different temporality, the three divergences in polarized partisan expression not only reflect and explain existing polarization concepts but also speak to the epistemological chasm between partisan groups. Our empirical analysis is based on Twitter discussion about the issue of immigration in the U.S. and applies topic modeling and time series analysis. Results demonstrate that liberal and conservative tweets exhibit different thematic emphases, are often spurred by different event features, and remain largely temporally independent, though both Trump’s tweets and emotionally evocative events can draw simultaneous reaction from both sides. These findings suggest that opposing partisan groups not only hold different views on the same issue, but also weave different events and facts about the issue into partisan expression in response to different exogenous factors. In short, they “talk past each other.” These polarized partisan expression patterns indicate a splintered public sphere, a concerning quality for deliberative democracy.
Full citation: Xiaoya Jiang, Yini Zhang, Jisoo Kim, Jon Pevehouse & Dhavan Shah (2023) Talking Past Each Other on Twitter: Thematic, Event, and Temporal Divergences in Polarized Partisan Expression on Immigration, Political Communication, DOI: 10.1080/10584609.2023.2263400
Full article available through Political Communication.