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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: onetzer@gsb.columbia.edu

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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.

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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.

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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.

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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 of the political spectrum, because we cannot identify the political affiliation of these users. These users may be swing voters or voters with a strong political affiliation who are political enthusiasts or wish to learn about the opinions voiced by the opposite political side.

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of users for whom we could identify their gender via their screenname4 (14,928,859 unique users).

Table Webappendix 2 provides descriptive statistics of our data.

One concern with using Twitter data is that a non-negligible proportion of the accounts are social

bots (Confessore et al. 2018). As bots might systematically generate personas that are associated

with conservatives or liberals, they may bias our results. Using a bot detection algorithm, we

removed over 2.2 million bot accounts (see Webappendix 4 for details). Interestingly, for both

political leaders we find a substantial proportion of bots (Hillary Clinton – 10%; Donald Trump

8%; GOP 5%; DNC 3%). An analysis without removing the bots using the Botometer algorithm

reveals similar results to the one reported below.

To measure the preference partisanship, we look at the joint followership of a brand

account and a political account by an individual (Culotta and Cutler 2016). As the number of

followers varies considerably across brands and political accounts, we control for these two factors

in measuring the preference partisanship. Our first measure controls only for the brand’s size as it

is used for analyses within a political account (e.g., the brands most commonly followed by

liberals). For this measure we calculate the ratio of the number of the brand’s followers that also

follow a Democrat (Republican) account to the total number of the brand’s followers – Relative

Preference Partisanship (RPP). This intuitively gives us a measure of the brands that are most

frequently followed by liberals or conservatives.

𝑹𝒆𝒍𝒂𝒕𝒊𝒗𝒆𝑷𝒓𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆𝑷𝒂𝒓𝒕𝒊𝒔𝒂𝒏𝒔𝒉𝒊𝒑𝒃𝒑(𝑅𝑃𝑃56) = #(:;<<;=>?@;:5&6)#(:;<<;=>?;:5)

, (1)

where b stands for a brand and p stands for a political account.

In our data, conservative accounts (Trump and GOP) have considerably more followers

than liberals (Clinton and DNC). We use the measure of lift to control for both the brand and

4 We use https://pypi.org/project/gender-guesser/ to assess gender. See Webappendix 3 for details.

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political account followership bases. We normalize the proportion of brand followers that also

follow a political account by the independent likelihood of following the brand and the political

account – Lift Preference Partisanship (LPP). We calculate the probability of following a brand

(𝑝𝑟𝑜𝑏(𝑏))by dividing the total number of brand followers of one brand by the number of account

followings in our sample of individuals who follow any brand and one political account. We

calculate the probability of following a political account (𝑝𝑟𝑜𝑏(𝑝))and the joint probability of

following a brand and a political account 𝑝𝑟𝑜𝑏(𝑏𝑎𝑛𝑑𝑝))in a similar manner. Intuitively, this

measure is the ratio of the likelihood of co-following a brand and a political account to the

likelihood of following both accounts independently.

𝑳𝒊𝒇𝒕𝑷𝒓𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆𝑷𝒂𝒓𝒕𝒊𝒔𝒂𝒏𝒔𝒉𝒊𝒑𝒃𝒑K𝐿𝑃𝑃56M =𝒑𝒓𝒐𝒃(𝒃𝒂𝒏𝒅𝒑)

𝒑𝒓𝒐𝒃(𝒃)×𝒑𝒓𝒐𝒃(𝒑). (2)

We further normalize the lift preference partisanship measure such that it sums to one

across the two political parties as follows:

𝑹𝒆𝒍𝒂𝒕𝒊𝒗𝒆𝑳𝒊𝒇𝒕𝑷𝒓𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆𝑷𝒂𝒓𝒕𝒊𝒔𝒂𝒏𝒔𝒉𝒊𝒑𝒃𝒑K𝑅𝐿𝑃𝑃56M =𝑳𝑷𝑷𝒃𝒑

(𝑳𝑷𝑷𝒃𝒑(𝑮𝑶𝑷)S𝑳𝑷𝑷𝒃𝒑(𝑫𝑵𝑪)). (3)

Results

The Mosaic of Preference Partisanship

Looking at the RPP (Equation 1) we can get a sense for the preference map of conservatives versus

liberals. In Figures 1 and 2, we depict the media brands, commercial brands and NPOs with the

highest proportion of Donald Trump, GOP, and DNC followers, respectively. First, in line with

the ongoing discussion about media bias and the political affiliation of media outlets, we find that

media sources differ drastically between conservatives and liberals. While media outlets such as

Vox.com, MSNBC and the Ebony Magazine are followed primarily by liberals, conservatives rely

mostly on the Drudge Report and Fox. Interestingly, outlets such as New York Times and CNN,

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that were attacked by Donald Trump to be a left-wing outlet spreading so called “fake news”

(Smilowitz 2017) do not appear to have high preference partisanship with the DNC. This could

indicate their role as mainstream media sources.

While media outlets are often expected to be associated with a political belief (Blake 2014),

the relationship between political affiliation and commercial brands is less clear. Figure 2 shows

that the top brands among Donald Trump’s followers are primarily characterized by golf brands

(e.g., Titleist, Masters Tournament, and Callaway Golf), alcohol brands (e.g., Miller Lite, Coors

Light, and Maker’s Mark), as well as financial services (e.g., SmartyPig and PIMCO). We find

strong similarity between the brands associated with the GOP and the brands associated with

Donald Trump, apart from a stronger focus of the GOP followers on security brands (e.g.,

Raytheon and Northrop Grumman) and more mainstream financial firms (e.g., Fidelity

Investments and The Vanguard Group). The brands followed by DNC followers are considerably

different from those followed by conservatives. Liberals have a diverse brand universe and seem

to focus on entertainment (e.g., Hulu, Showtime, Syfy), tech companies (e.g., Lyft and Hulu) and

retail (Barnes&Noble and Pier 1 Imports; see Figure 1).

Looking at the difference between the NPOs followed by followers of the different political

groups can provide insight into the set of values that are important to conservatives and liberals.

Top NPOs followed by conservatives are the National Rifle Association, veteran NPOs, such as

Disabled American Veterans and Wounded Warrior Project, the Christian nonprofit organization,

Billy Graham Evangelistic Association, and the habitat conservation and hunting organization,

Ducks Unlimited. On the other hand, liberals focus more on causes related to reproductive health

care (e.g., Planned Parenthood), the immigration-related civil rights organization, American Civil

Liberties Union, and environmental organizations (e.g., Environmental Defense Fund and the

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Sierra Club). These strong differences highlight the relationship between political affiliation and

societal values.

To depict the full mosaic of the different worlds liberals and conservatives live in, Table 1

presents the top brands in terms of the proportion of brand followers that follow the GOP or the

DNC across a set of categories. The results emphasize the stark difference between the microcosm

of conservatives and liberals. To more systematically assess the mosaic of preference partisanship,

we apply a topic modeling approach, which looks for groups of accounts that tend to be co-

followed together by users and can thus be interpreted as topics reflecting a user persona.

Specifically, we apply the unsupervised machine learning Hierarchical Poisson Factorization

approach (see Webappendix 5 for details) to identify such persona topics. For each topic, we

calculate the average likelihood of followership for each user. We then average these likelihoods

across users and their political affiliation to get the political affiliation of the topic.

We identify 31 non-empty topics. The topics vary considerably in their degree of

polarization. Table 2 displays the topics that were most closely related to the DNC or GOP. We

identify several topics with strong affiliation to the political parties. For example, Topic 3 reflect

a liberal persona that combines strong societal and nature value based on both NPOs and media

sources. Similarly, Topic 5 reflects a liberal younger tech, health and entertainment persona. On

the other hand, Topic 6 reflects a conservative sport enthusiast based on media and sport

followerships. Similarly, Topic 8 reflects a conservative financial persona based on both financial

media and financial institutions’ followerships (see further details in Webappendix 6).

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Is Twitter Followership a Good Measure of Political Affiliation and Preference Partisanship?

One may question the relationship between following a brand or a political account on Twitter and

the user’s preference or political ideology. Users may follow an account on Twitter for reasons

other than preferences such as to obtain information, due to employment with the company, or for

social reasons. Additionally, it is possible that Twitter users are not representative of the population

of consumers at large. We conducted multiple robustness analyses to examine the validity of

Twitter followership as a measure of preference partisanship.

1. Political affiliation – For a subset of over 1 million users we compared the Twitter political

party followership based on our data with the political ideology score of Barberà et al.

(2015). We find that following the political party on Twitter is strongly related to the users’

political ideology score (see Webappendix 1 for details).

2. Brand preferences partisanship and brand affiliation – We conducted several analyses to

validate our brands’ political affiliation measures. First, we compare our brand preference

partisanship measures to the political affiliation of a subset of 283 brands that are also

tracked by the consumer panel data company YouGov. YouGov surveys consumers daily

on various topics including their brand preferences, purchase intentions and political

affiliation. We find a high correlation between our Twitter followership data and the

corresponding relative brand preferences by political affiliation based on the YouGov

survey (r=0.75). Thus, despite the possible limitations of the Twitter platform in terms of

representing consumers’ preferences at large, this analysis demonstrates that Twitter

followership strongly correlates with preference partisanship as measured by a survey of a

representative sample of U.S. consumers who were asked about their brand preferences

(See Webappendix 7 for the details).

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Second, we relate our measure of brand political affiliation, to political donations made by

these brands. Specifically, for a subset of 360 brands we obtain from

https://www.goodsuniteus.com political donations made by the brand and its senior

employees. We find a high correlation between the RLPP and the proportion of donations

to the political party (r=0.4). Thus, the preference partisanship of a brand seems to be

affected by, or at the minimum correlated with, the brands political donation distribution.

3. Media preference partisanship - To check the robustness of our media preference

partisanship we compare our preference partisanship for media outlets with the political

bias ratings of media outlets according to mediabiasfactcheck.com. We find a high

correlation (r=0.74, p<0.001) between our media RLPP and the website ratings (see

Webappendix 8 for details).

Overall, these analyses validate that our political ideology and brand preferences as measured by

Twitter followership are highly correlated with external measures of political affiliation and brand

preferences from different sources.

What Happened to Preference Polarization Post the 2016 Election?

The election of Donald Trump and its effect on political partisanship have become increasing

topics of interest. A pattern discussed heavily in public media post the 2016 U.S. election has been

the increased polarization between conservatives and liberals (Gentzkow 2016, Pew Research

Center 2017b). Thus, we might expect to also see an increase in brands preference polarization

post the 2016 election. To investigate this issue we analyze the change in polarization in preference

partisanship over time.

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We measure polarization between conservatives and liberals using the entropy of the

RLPP. The entropy measure for brand b at time t is calculated as (see Webappendix 9 for details

of the Entropy calculation):

𝑬𝒏𝒕𝒓𝒐𝒑𝒚𝒕𝒃 = −∑ [(𝑅𝐿𝑃𝑃\56) × logK𝑅𝐿𝑃𝑃\56M`.6∈cde,fgh (4)

Note that at a RLPP of 50% the brand would exhibit no partisanship and maximal entropy.

We compare the entropy measure for brands in February 20175 and the polarization a year later

(February 2018). Note that in calculating polarization we hold Twitter users’ political affiliation

constant per February 2017 and only look at changes in brands’ followership to avoid change in

partisanship post-election (e.g., users who start following the GOP post-election).

Looking at the overall change in entropy for all commercial accounts, we find an increasing

polarization. Of the 637 brands 70% show an increased polarization, while only 30% of the brands

decreased in terms of polarization during the year following the election (see Figure 3). The

polarization is stronger for media outlets (83% of the 66 media outlets exhibit increased

polarization) followed by NPOs (76% of the 71 NPOs exhibited increased polarization) and

commercial brands (67% of 500 brands exhibited increased in polarization). All three groups show

a significant increase in polarization. This result emphasizes that the proclaimed increase in

polarization commonly discussed in the context of people’s political affiliation may extend further

into people’s lives than initially thought, manifesting itself by a stronger division in brand

preferences.

Next, we compare the change in polarization post the 2016 election between liberal and

conservative brands as measured by their preference partisanship right after the election. We find

5 For NPOs we use the data from April 2017 rather than February 2017 because we started tracking NPOs in April 2017.

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that the overall increase in polarization is driven by liberal brands. 78% of the brands that were

identified as DNC brands in February 2017 exhibit an increase in polarization, whereas those

identified as GOP brands in February 2017 even show a decrease in polarization albeit

insignificant.

The stronger increase in polarization for liberal brands compared to conservative brands is

consistent with the mechanism of compensatory consumption which is often used to combat

identity threats (Coleman et al. 2018). Indeed, liberals faced a threat to their political identity after

the somewhat unexpected election of Donald Trump, which they compensated by a stronger

support for liberal oriented brands and growing activism (Green 2018). This behavior can explain

our findings that liberals looked for consumption of media sources, support of NPOs and even

commercial brands that would help to offset the threat to their identity (Rucker and Galinsky 2013,

Long et al. 2018). Such compensatory consumption has been shown for a variety of domains such

as threats to one’s masculinity, intelligence, power, personality and freedom but thus far not to

political ideology.

Post the 2016 election, we witnessed an increase in occasions in which brands have either

publicly opposed or supported Donald Trump’s policies such as the anthem kneeling protest by

many NFL teams, Nordstrom discontinuing Ivanka Trump’s product line, or Under Armour

endorsing Donald Trump. Additionally, many brands, such as Google or the New York Times,

have been actively attacked by Donald Trump. Accordingly, we aim to further understand whether

the polarization displayed above changed for brands that actively or passively took a political

stand. We can classify brands into three different groups, brands that did not take any action,

brands that took (or were passively associated with) a congruent action (i.e., liberal brands

opposing Donald Trump or conservative brands supporting Donald Trump), and brands that took

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(or were passively associated with) an incongruent action (i.e., liberal brands supporting Donald

Trump or conservative brands opposing Donald Trump). We should expect that congruent actions

should be associated with an increased polarization and incongruent actions with a decreased

polarization.

As illustrated in Figure 4, our results indeed show that brands that took a political stand

congruent with their political affiliation became significantly more polarized (79% of the 86 brands

became more polarized), while brands that took an action incongruent with their political

affiliation (e.g., NFL teams who took a liberal stand albeit mostly conservative preference

partisanships) became significantly less polarized (78% of the 46 brands). Consistent with our

previous finding we find that the majority of brands that took no political action became

significantly more polarized post the 2016 election (73% of the 505 brands), driven by the liberal

brands. The polarization change for conservative brands which did not take actions is statistically

insignificant. Regarding brands that took actions, we find that polarization increased for brands

that were associated with the DNC and engaged in congruent actions, while brands that took

incongruent actions (in support of Donald Trump), saw a decrease in polarization. For brands that

were associated with GOP followers, we find that congruent actions increased polarization, but

incongruent actions were associated with a decrease in polarization.

In summary, our results highlight that the widespread claim of increased political

polarization in the U.S. population is mirrored in an increasing polarization in preference

partisanship. Interestingly, we find a stronger polarization for liberal brands, which can be

attributed to the mechanism of compensatory consumption in which liberals use their followership

of brands to overcome identity threats created by the 2016 election. Additionally, we find that

brands that took an active or a passive political stand incongruent with their political affiliation

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saw a decrease in polarization. Generally, our results on brands taking a political stand indicate

that brands can take control of their political polarization.

What do Conservative and Liberal Brands Have in Common?

Is there something that SmartyPig, Chick-fil-A, and Lowe’s have in common that makes them

attractive to followers from the conservative side of the political map? As we show later, these

brands are characterized as being “rugged”, “energetic” and “down-to-earth?” while brands like

Lyft, Chobani and MoneyGram, which are mostly followed by liberals, are perceived as

“intelligent” and “socially responsible”. These commonalties go beyond demographic similarities

of the brand followers and towards brand characteristics and personality.

To better understand the commonalities of the brands that are followed by individuals at

the different ends of the political spectrum, we collected a set of brand characteristics. Specifically,

we obtained survey data from the marketing agency Y&R conducted with a large U.S. consumer

panel. Brands are rated on a host of human personality characteristics such as “authentic,” “leader,”

and “intelligent” (Aaker 1997). For a subset of 447 brands we calculate for each brand the average

brand personality over the four quarterly surveys of 2016 for 46 brand personality traits. We then

split brands into liberal and conservative based on RLPP and calculate the average brand

characteristics for conservative vs liberal brands. We use an ANCOVA to control for gender by

using the proportion of female brand followers as a covariate.

Figure 5 displays the brand characteristics that show significant difference between DNC’s

and GOP’s as well as between Donald Trump’s and Hillary Clinton’s brands. First, consistent with

Ordabayeva and Fernandes (2018) who showed that liberal consumers prefer unique brands and

use them as a mean to differentiate themselves from others, we find that independence, difference

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and innovation are significantly more associated with DNC brands relative to GOP brands.

Additionally, we find that liberal brands are characterized as being trustworthy, trendy, intelligent

and progressive. On the other hand, brands preferred by conservatives tend to be more strongly

characterized as traditional rugged, energetic, down to earth and arrogant.

The Role of Demographics in Preference Partisanship

One pattern that emerges from our results is that liberal brands might be perceived as somewhat

more feminine (e.g., Burt’s Bees) and conservative brands may be perceived as more masculine

(e.g., Cabela’s) or more associated with conservative geographies (e.g., Chick-fil-A). First, it is

unlikely that gender effects alone can explain the political partisanship as the voting patterns for

Donald Trump vs. Hillary Clinton in the 2016 election was not very divergent (39% of Trumps’

validated voters were female and 46% of Clinton’s validated voters were male, Pew Research

Center 2018).

Second, to fully control for geography and gender, in Figure 6, we contrast the RPP for the

DNC and GOP separately for females and males who reside in NY and Pennsylvania.6 Consistent

with our previous results, we see substantial differences in preference polarization even when

controlling for gender and geography. For example, whereas both DNC and GOP females in NY

follow retail brands, the stores they prefer diverge substantially. DNC females follow stores like

ModCloth and Barnes&Noble, while GOP females follow stores like Home Goods,

Bloomingdales, or the TV shopping channel QVC.

To investigate more systematically the differences between conservative and liberal

preference partisanship, we explore the preference partisanship variance explained by “objective”

6 Analysis of additional geographies is available from the authors.

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characteristics such as demographics (gender and geography of the followers) and firm

characteristics such as location of the headquarters and color of the firm’s logo, as well as more

perceptual drivers, namely the personality characteristics (see Webappendix 10 for details of the

variables used). We regress the GOP RLPP (Equation 3) on the demographics, firm characteristics

and personality. We also repeat the analysis for Donald Trump (Webappendix 11).

We first look at the model that includes only the objective brand characteristics (see Table

3). We see that a 10% increase in the proportion of female followers of the brand corresponds to a

3% decrease in the RLPP for the GOP. As expected, the proportion of conservative voters in each

state weighted by the popularity of the brands in the state also increases the preference partisanship

for conservative brands. However, we find no significant relationship between domestic brands

and the GOP brand affiliation. Interestingly, we do find that domestic brands significantly increase

the preference partisanship for Donald Trump by 0.4% (see Webappendix 11).

Taken together, our results show that gender and location can explain nearly 50% of

variation in the brands’ political affiliation. Additionally, a model that includes the brand

personality characteristics alone is equally effective in explaining the variation in the preference

partisanship. Adding the perceived brand personality characteristics to the demographic variables

can add additional information over and beyond the information captured by gender and the

location of the brand’s users explaining overall nearly two thirds of the variation in preference

partisanship.

Discussion

Our results highlight that the U.S. political divide extends to the preferences of individuals for

media sources, nonprofit organizations and commercial brands. We additionally find evidence for

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an increasing polarization in preference partisanship since Donald Trump became President of the

U.S. This polarization is stronger for media outlets and NPOs compared to commercial brands.

Interestingly, the increase in polarization is primarily driven by liberal brands’ followers. This

finding is in line with the growing activism of liberals post-election. Furthermore, brands can take

control of their degree of polarization by taking a political stand. Finally, we identify systematic

differences and commonalities between the preference partisanship of liberals and conservatives.

While gender and geography can explain an important part of the preference partisanship

differences between the two sides of the political spectrum, brand characteristics can further help

to understand preferences of liberals and conservatives. This is a particularly relevant insight for

companies as brand characteristics are consumers’ perceptions that – at least to a certain degree –

are in the control of a company. Thus, brands should both measure and, if needed, attempt to adjust

their political affiliation as such affiliation has important implications for consumers’ preference

(Ordabayeva and Fernandes 2018, Kim, Park, and Dubois 2018).

An important contribution of our work is demonstrating the value of using publicly

available social media data to infer both consumers’ preferences and map preference partisanship.

Such an endeavor was effortful, costly and unscalable in the past. Twitter provides a useful

platform to access such data. However, one may question the generalizability of Twitter data to

infer consumers’ preferences. While we have conducted multiple analyses contrasting Twitter data

with survey and other outside measures of preference and political affiliation and found strong

convergent validity, we leave for future research to explore how preference partisanship can be

extracted from other sources of data that may include, for example, purchase data (e.g., e-

commerce data).

19

Finally, to enhance the access to our data, analyses, and the dissemination of our findings

we created a publicly available API that allows access to our results. We hope this offers a valuable

source for brand managers, consumers, academics, journalists, and political parties:

http://www.social-listening.org/.

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Figures

Figure 1. Brands, Media and NPOs Sharing Most Followers with GOP or DNC Political Accounts on Twitter

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Figure 2. Brands, Media and NPOs Sharing Most Followers with Donald Trump on Twitter

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Figure 3. Changes in Polarization Post the 2016 Election

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Figure 4. Changes in Polarization by Political Action

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Figure 5. Differences in Brand Perception between Liberal and Conservative Brands

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Figure 6. Brands, Media and NPOs Sharing Most Followers with GOP or DNC Political Accounts on Twitter for New York and Pennsylvania Females and Males

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Tables

Table 1. Mosaic of Conservative and Liberal Brands across Categories

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Table 2. Hierarchical Poisson Factorization Topics and Preference Partisanship

Table 3. Preference for GOP by Brand Characteristics, Firm Characteristics, and Demographics