# Socioeconomic Correlates of Anti-Science Attitudes in the US
## 1 Introduction  
Responding to societal crises requires sustained changes in individual behaviors [1, 2, 3]. For example, to slow the spread of the COVID-19 pandemic, governments worldwide have enacted a variety of mitigation measures that include physical distancing, a ban on public gatherings, quarantines, and mask mandates. However, public response to these policies was influenced by factors such as political partisanship and attitudes towards science [4, 5]. While conservatives were more skeptical of government policies to limit the spread of the virus [6], mistrust of authorities and experts extended beyond partisanship [7, 8]. mistrust of science unites a diverse coalition of \"anti-vaxxers\", \"natural parenting\" and alternative medicine advocates, and others who are suspicious of \"big government\" and \"big pharma\" that refuse to follow some of the official health guidelines. Understanding science skepticism will help us better frame policies that encourage the behaviors necessary to respond to the next health or environmental crisis [9]. Anti-science attitudes are typically assessed through surveys, which ask people to report how much they agree with scientific statements about vaccine safety or human activity causing climate change, for example. Using the responses, researchers have examined the role of political ideology [8] and religious views [10]. Researchers have linked attitudes toward science to scientific literacy, showing that scientific knowledge explains anti-science views while controlling for key socioeconomic variables such as religious faith, gender, and income [11, 12, 13]. More recent research has examined cognitive and emotional factors, such as reasoning ability [14, 15], fear [16], and anger [17]. In this paper, we present a framework for measuring anti-science attitudes that are expressed through messages posted on social media. This approach can complement existing methods [18; 19; 20] by enabling us to study attitudes at the population scale with high spatial and temporal granularity, as well as measure their evolution over time. We illustrate this through the framework of studying the social and cognitive factors of the communities where users live that explain the prevalence of anti-science attitudes. Researchers have previously used social media data to characterize the political ideology of users. [PERSON] et al. [21] introduced a text-based approach, using sets of political terms to measure partisanship. In contrast, [PERSON] [22] applied a latent space model to the follower graph, while [PERSON] et al. [23] used label propagation on the retweet graph to infer the political leanings of social media users. More recently, researchers leveraged a set of curated news and information sources to label the political orientation of users based on the partisanship of the information sources they share [24; 25]. Others have operationalized users' vulnerability to fake news and misinformation based on their attention to low-quality information sources [26; 27; 28]. We adopted a similar approach to characterize science skepticism, using the number of links to curated pro-science and anti-science information sources users share on social media as a measure of their attention to anti-science content. Specifically, we used a random 10% sample of all messages (tweets) publicly shared on Twitter in October 2016. We further restricted the tweets to those with location metadata and linked them to counties within the US. We trained machine learning classifiers to infer users' attitudes towards science based on the information sources they shared. In addition, we inferred emotions expressed in tweets and also linked data to political partisanship through county-level outcomes in the 2016 presidential election. To explain variation in attitudes toward science, we studied their relationship to the socioeconomic characteristics of communities (counties) from which people tweeted.