Explaining the ‘Trump Gap’ in Social Distancing Using COVID Discourse

Austin Van Loon, Sheridan Stewart, Brandon Waldon, Shrinidhi K Lakshmikanth, Ishan Shah, Sharath Chandra Guntuku, Garrick Sherman, James Zou, Johannes Eichstaedt

Keywords: computational social science, social distancing, word2vec, vector semantics, twitter, bert

TL;DR: In U.S counties where residents social distance less on average, COVID discourse on Twitter is more indicative of associations between the virus and the concepts of fraud, the political left, and more benign illnesses such as the flu.

Abstract: Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities' response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the ``"Trump Gap", or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.