Explaining the Trump Gap in Social Distancing Using COVID Discourse (2020)

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.

## introduction the covid-19 pandemic is one of the most significant and devastating events in human history, with over thirty-five million confirmed cases and over one million deaths globally (as of october 6, 2020). part of the reason this virus has claimed so many lives is that individuals and the communities they are embedded in vary in the degree to which they take the virus seriously and follow suggested governmental guidelines aimed at reducing the spread of the virus, including the practice of social distancing. the degree to which the virus will continue to spread-especially as we await a second wave of infections-will depend not only on our biological understanding of the virus, but also on our understanding of the social factors that govern the degree to which everyday individuals are willing to and do in fact engage in coordinated efforts to help slow the spread of the virus. an especially interesting case of response to the virus is the united states, the global leader in coronavirus deaths at the time of writing. variation in response to the pandemic in the united states is particularly interesting because it does not appear to be randomly distributed, but in fact highly predictable. political party identification in particular seems to be strongly associated with the degree to which individuals endorse health behaviors recommended by authoritative organizations such as the cdc and who @xcite . we seek to build on this work by identifying, at least in part, the reason why we observe these behavioral disparities. rejecting these health behaviors is not definitionally related to identifying as a republican or supporting donald trump. we contend instead that in addition to the virus's objective properties, sars-cov-2 has taken on heterogeneous socially constructed meanings @xcite , which vary over the population and shape communities' response to the pandemic. we further contend that this variation in how people understand the virus partially accounts for the link between political party identification and these behavioral outcomes. research in sociology and anthropology (e.g. @xcite finds that the meanings humans associate with different concepts tend to cluster in groups. one way that these meanings are clustered is by geography. here, we leverage geographic variation in the content of discourse related to covid-19 over the social media platform twitter to explore the relationships between political identity, the potentially variable meaning of covid-19, and social distancing. to do this, we capture elements of what covid-19 means to different populations using word embeddings, a technique that has been demonstrated to measure widely-held cognitive associations in groups @xcite @xcite . this approach allows us to pick up on the disparate ways covid-19 is interpreted by the residents of different u.s. counties. further, we use google mobility report data 1 to demonstrate that these disparate meanings both correlate with the degree to which individuals stay at home, our indicator of social distancing, and accounts for a substantial proportion of what we call the "trump gap," or the tendency toward less social distancing within counties that exhibited greater support for donald trump in the 2016 presidential election. first, using word2vec to capture interpretable, theoretically meaningful differences in meanings attributed to the virus, we find that counties in which the virus is discussed in a way that is semantically similar to the concepts (a) the political left, (b) fraudulence, and (c) the flu and the common cold are less likely to social distance. we further corroborate these findings with a minimal pair analysis, demonstrating that a bert-based deep learning model trained on our data learns to associate social distancing with (a) blaming the pandemic on the political left, (b) questioning the reality of the virus, and (c) likening the virus to cold or flu. finally, we demonstrate with mediation analysis that variation in the meanings associated with the virus mediates nearly one-fifth of the association between support for donald trump in the 2016 presidential election and social distancing at the county level. ## theory-driven word2vec analysis we measure semantic associations between words as they are used in a county's covid discourse using the word2vec word embedding algorithm @xcite . the algorithm places each word that appears in a corpus in a high-dimensional space where the proximity of two words in that space is proportional to the similarity of the words that appear in the linguistic contexts of those words. more intuitively, the word2vec algorithm creates a k-dimensional space in which semantically similar words, defined roughly as the interchangeability of those words, appear closer to one another than do semantically dissimilar words. we measure the semantic similarity of any two words by taking one less the cosine distance of the vector representations of the words' positions within the word embedding space. using word2vec to reliably measure variation in meaning is difficult because the algorithm requires a large amount of text to accurately represent the meaning of a word in any one context and because spaces that result from applying the algorithm to disparate corpora do not necessarily align straightforwardly. many existing solutions to these issues make difficult to verify assumptions about the process by which language is generated. we combat these issues with a novel, word2vec-based measurement strategy which maintains the algorithm's non-parametric properties. this strategy, illustrated in figure 1 , first involves taking a fixed number of randomly selected tweets from each county and building a "baseline model." this baseline model leverages text from all counties to build a corpus large enough to train a reliable word2vec model and reflects semantic relations consistent across our corpus. then, for each county, we fine-tune that baseline model to create a "county-specific model," which leverages a new sample of text from each county as well as the semantic information encoded in the baseline model to build an embedding space which reflects the county's potentially idiosyncratic semantic relationships. in order to make distances across different county-specific models more comparable, we measure the semantic similarity between words as the difference between the similarity of two keywords (e.g., "coronavirus" and "hoax") in the county-specific model less the similarity of the same word pair in the baseline model. this approximates measuring how counties deviate from the central tendency in their semantic association between words. imagine a scenario in which we have a population of one hundred counties, and we are interested in the degree to which residents of each county semantically associate the words "sky" and "blue." further imagine that in ninety-nine of the one-hundred counties, these words are strongly associated and similarly so, but that in one county these words are much more weakly associated. 6 our hope with this measurement strategy is that the semantic similarity between "sky" and "blue" in the baseline model (which again is built from a balanced random sample of texts from each county) would mostly reflect the ninety-nine counties in which "sky" and "blue" have relatively high semantic similarity, but that the county-specific models, each of which is fine-tuned on all text produced by its corresponding county, would more faithfully reflect each county's own, potentially unique, semantic relationships. then, the difference between the semantic similarity of two words in the baseline model and any county-specific model would reflect how the county's own semantic relationships differs from the central tendency of all counties. in this scenario, we would expect a relatively small, positive deviation from the central tendency for the semantic similarity of "sky" and "blue" for ninety-nine of the one-hundred counties, and a much larger, negative deviation from the central tendency for the one outlier county. this strategy allows us to reliably capture variation in the degree to which different counties semantically associate these two words, even though we might not have a sufficient amount of text data to train a from-scratch word2vec model for any particular county. to combat the stochasticity inherent to the word2vec algorithm as well as in sampling texts to build the baseline model, we create fifty separate baseline model/county-specific model pairs using random samples of one-thousand tweets from each county and take the average delta similarity score across all fifty. finally, in accordance with related literature, we measure semantic relationships between concepts using a set of related words. the concepts of interest to us include the coronavirus ("coronavirus" and "covid"), the political left ("democrat", "democrats", and "liberals"), relatively benign illnesses ("flu", "influenza", and "cold"), and fraudulence ("hoax", "fake", and "scam"). we standardize (mean center and rescale to have a standard deviation of one) each of these measures to ease interpretation. ## minimal pair analysis observational results like the ones reported in to partially address this, we train a deep-learning model to predict social distancing in a county from tweets originating from that county, then feed synthetic texts into that model that we systematically manipulate to only vary on the linguistic feature of interest. we assess whether the model has "learned" to associate social distancing with the same linguistic features we explored in section 3, i.e. likening the virus to the flu, alleging that the pandemic is a hoax, or implicating the u.s. political left in the spread of the disease. ## mediation analysis we have shown that variation in the meanings attributed to the virus are associated with social distancing using two different methodological approaches. now we test whether meanings attributed to the virus mediate the relationship between trump vote margin in the 2016 election and social distancing. as we note above, trump vote margin at the county level in the 2016 presidential election is strongly associated with an increase in social distancing in the early days of the covid-19 pandemic. indeed, when regressing our measure of social distancing on trump vote margin only, we get an in-sample r 2 of 0.38. the exact set of mechanisms linking trump vote margin to social distancing, however, remains unknown. mediation analysis allows us to test individual mechanisms, i.e. whether a given variable (the mediator) "explains" the relationship between the other variables. put differently, mediation analysis seeks to identify whether part of the "total effect" of an independent variable on the outcome is accounted for by an "indirect effect," or an effect on the mediating variable which subsequently affects the outcome. we use mediation analysis to formally test whether measures of the meanings attributed to the to confirm the analyses presented in section 3. next, we reduce the dimensionality of this matrix by estimating its top three principal components, creating three orthogonal measures that explain the most variation in these one-thousand measures. these three dimensions succinctly describe the variation in meanings attributed to the virus in each county in our analysis. when regressing our social distancing measure on these dimensions simultaneously, each coefficient is statistically significant at the @xmath0.001 level and the model has an in-sample r 2 of 0.36. in order to test whether these measures of the meanings attributed to the virus mediate the relationship between trump vote margin in the 2016 election and social distancing in the early days of the pandemic, we use structural equation modeling (sem) and proceed in two stages. in the first stage, social distancing is regressed on trump vote margin alone, providing an estimate of the total effect of trump vote margin on social distancing. in the second, social distancing is regressed on trump vote margin and all three principal components. ## discussion and conclusions in our theory-driven analyses using word2vec, we demonstrated that counties social distanced less the more their covid-19 discourse was indicative of cognitive associations between the virus and the concepts of (a) the political left, (b) fraud, and (c) less serious illnesses. we reaffirmed these findings using a deep learning model and bert, showing that a model trained on our data learned to predict less social distancing for synthetic counties which (a) blame the political left for the pandemic, (b) question the reality or severity of the pandemic, and (c) liken the virus to the flu, compared to control versions of these counties. through mediation analysis, we further showed that the heterogeneity in meanings the virus took on across the u.s. derived through pca explains almost 20% of what we call the trump gap, i.e. the empirical pattern that counties where residents supported donald trump in the 2016 election more social distanced less. while it is impossible to rigorously identify a causal effect with the present analyses, we mitigate concerns about spuriousness by using withincounty variation in social distancing as well as by controlling for several potential confounding variables. our experiments described in section 4 further mitigate these concerns. additionally, we establish temporal precedence by using the change in the outcome measured after the language variables. as always, omitted variables may be present; our analyses are suited to making strong claims about causality. overall, our results confirm that the different meanings attributed to the virus are associated with individuals' tendency to social distance. in fact, we demonstrate with mediation analysis that these meanings explain a great deal of the association between support for trump in 2016 u.s. presidential election and social distancing, an empirical regularity that has been demonstrated (typically at the individual level) elsewhere. this means that if public health officials hope to increase adherence to social distancing (and potentially other health behaviorrelated) guidelines in the future, they must be mindful of the various meanings attributed to the virus in different communities, and attend to the dynamic process by which it might acquire new meanings. this study outlines an approach to measuring the variation in meaning attributed to a novel concept in a population when limited text is available for each sub-population and builds on recent literature introducing minimal pair analysis of synthetic text. additionally, our results demonstrate the importance of the process of the social construction of meaning, in accordance with arguments championed by sociologists for over a century. finally, it demonstrates that large-scale social media text can be mined productively to recover traces of this collective meaning-making, and that, in principle, this can be done quickly enough to inform public health policy and messaging.