Polarized America: From Political Partisanship to Preference Partisanship Verena Schoenmueller (Bocconi University), Oded Netzer1 (Graduate School of Business, Columbia University), and Florian Stahl (University of Mannheim, Mannheim Business School) Abstract In light of the widely discussed political divide post the 2016 election, we investigate in this paper whether this divide extends to the preferences of individuals for commercial brands, media sources and nonprofit organizations and how it evolved post the election. Using publicly available social media data of over 150 million Twitter users’ brand followerships we establish that commercial brands and organizations are affiliated with the consumers political ideology. We create a mosaic of brand preferences that are associated with either sides of the political spectrum, which we term preference partisanship, and explore the extent to which the political divide manifests itself also in the daily lives of individuals. Moreover, we identify an increasing polarization in preference partisanship since Donald Trump became President of the United States. Consistent with compensatory consumption theory, we find the increase in polarization post-election is stronger for liberals relative to conservatives. From a brand perspective, we show that brands can affect their degree of the political polarization by taking a political stand. Finally, after coloring brands as conservative or liberal we investigate the systematic differences and commonalities between them. We provide a publicly available API that allows access to our data and results. Keywords: Political Marketing, Social Media, Data Mining, Political Polarization, Branding. 1 Corresponding Author: 3022 Broadway, Uris 520, New York, NY 10027, USA, Telephone +1-212-854-9024, Email: [email protected] 1 Introduction The political divide in the United States (U.S.) is well documented and has been claimed to accelerate over the past couple of years (Pew Research Center 2017a). In the aftermath of the 2016 U.S. presidential election, a heated debate arose regarding the role of social media in American politics and its impact on the political divide. Part of this debate has revolved around the political echo chambers, suggesting that people surround themselves with likeminded people, leading to a further enhancement of the political divide. Dave Barry, the Pulitzer Prize winning author and columnist, anticipated the current climate well when he suggested that Republicans think of Democrats as godless, Nordstrom-loving, weenies who read The Atlantic while sucking their latte, while Democrats dismiss Republicans as ignorant religious fanatics, NRA-obsessed, drinking Budweiser, watching Fox News and surfing the Drudge Report (Barry 2004). In line with this claim, the far-reaching impact of political orientation has been shown to relate to many different aspects of lives, such as the person’s social identity (Iyengar and Westwood 2015, Ordabayeva and Fernandes 2018, Ordabayeva 2019), personality (Sibley et al. 2012) and even to physiological characteristics such as one’s genetics (Alford et al. 2005) and neurological structures (Nam et al. 2017). The view that the political divide extends beyond political partisanship to day-to-day behaviors, is also echoed by several studies that have shown that conservatives and liberals2 exhibit different patterns of behavior in grocery shopping (Khan et al. 2013), movie choices (Roos and Shachar 2014), recycling (Kidwell et al. 2013), charity (Winterich et al. 2012), complaint/dispute (Jung et al. 2017), and lifestyle choices (DellaPosta et al. 2015). The question we ask is, can we use publicly available social media data to put together the pieces of evidence from the above-mentioned literature into a comprehensive mosaic that 2 Throughout the paper, we interchangeably use liberals and Democrats as well as conservative and Republicans to reflect the two sides of the political spectrum. 2 reflects the differences between conservatives and liberals that span far beyond their political differences, which we term preference partisanship? Social media in general, and Twitter in particular, played an important role in the 2016 U.S. presidential election and as an important and controversial communication channel thereafter (Emerging Technology 2017). It has been accused by some for fueling political divide by information sharing (Bail et al. 2018), though others have suggested its effect is limited (Boxell et al. 2017). Independent of the role of Twitter in directly affecting individuals’ political opinions, we explore Twitter’s role as a window into people’s preferences, beliefs, and values, creating a picture of one’s persona (Culotta and Cutler 2016) and relating it to political ideology. On social media platforms such as Twitter, individuals “announce,” via the accounts they follow, their preferences and values with respect to the stores they like to shop in, the sports team they root for, the newspapers they read, their alcoholic beverage of choice or the charity organizations they support. This source of data is not only extensive and large in scale, but it is also publicly available at the individual Twitter user level. We use these data to identify political affiliation of a brand or organization, by the overlap of followers of Democratic accounts (Hillary Clinton and/or the Democratic National Committee (DNC)) and Republican accounts (Donald Trump and/or the Grand Old Party (GOP)) and their brand/organization followership. We then separate Twitter users who follow accounts of the two opposite ends of the political spectrum to obtain the preference partisanship map. Particularly for a sensitive topic such as political affiliation, such an effort was not possible before due to limited data on a large body of individuals with respect to both their political affiliation as well as a wide spectrum of their preference microcosm. 3 Accordingly, the objective of this paper is to use social media data to investigate the degree to which the political divide stretches to consumer preferences as expressed by their social media behavior. Specifically, in this paper we address the following four research questions: • Does the U.S. political divide extend to preferences for commercial brands, media sources, and non-profit organizations (NPOs)? And if so, can we use readily available social media data to uncover preference partisanship? • How did the preference partisanship polarization evolve post the 2016 U.S. election? • In light of the increase in brands taking a political stand post the 2016 elections, how do such actions affect the brands’ preference partisanship? • Are there systematic differences and commonalities between the underlying preference universes of Democrats and Republicans? To provide an easy access to the U.S. preference partisanship, we developed a publicly available API that can be used by brand managers, researchers, writers, and consumers to assess and compare the extent to which brands are preferred by conservatives or liberals on the Twitter platform. Dataset and Measures of Preference Partisanship We build a dataset of all Twitter users who follow one of 637 major brand accounts that have more than 100,000 followers, which we collected since February 2017. We use brand account to refer to non-personal Twitter accounts such as companies, sports teams, media outlets, and NPOs. The selection of accounts to follow is based on brands that are tracked by Y&R Brand Asset Valuator and Interbrand, which track brands that are of high relevance to consumers, thus creating a broad map of consumers’ preferences. 4 We restrict our analysis to Twitter users that follow one of the brands and at least one Democratic (Hillary Clinton and/or the DNC) or one Republican (Donald Trump and/or the GOP) Twitter account, and who do not simultaneously follow Democratic and Republican accounts. This restriction allows us to focus on users with a clear political preference.3 Previous studies have demonstrated that the exclusive followership of Donald Trump and Hillary Clinton on Twitter is a reliable indicator of the user’s support for the candidate (Electome.org). We validate our measure of political ideology with the ideology score of Barberá et al. (2015), who collected a large dataset of political ideology of Twitter users implementing a latent space model of political ideology (see Webappendix 1). We find a high correlation between our political affiliation metrics and the ideology score of Barberá et al. (2015). Specifically, we find a higher correlation for following the political party (DNC or GOP) and the users’ political ideology scores than for following the party leader (Donald Trump or Hillary Clinton). Thus, throughout the paper we mostly focus on followership of the political parties’ accounts to contrast liberals and conservatives. We further contrast the GOP’s followers to Donald Trump’s followers to investigate differences between conservative political ideology and followership of Donald Trump as an individual (Smeltz 2018). Our dataset includes 24,258,153 unique followers that exclusively follow one of the political accounts with a total of 152,873,846 observations (account followings). To investigate differences in preference partisanship between gender and location, we access geographical location for the subset of users who reported the U.S. state they belong to (2,112,571 unique users) and the gender 3 We do not include users who follow political accounts from both sides