Trait Associations for Hillary Clinton and Donald Trump in News Media: A Computational Analysis
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Special Section Article
Social Psychological and
Personality Science
Trait Associations for Hillary Clinton 2018, Vol. 9(2) 123-130
ª The Author(s) 2018
Reprints and permission:
and Donald Trump in News Media: sagepub.com/journalsPermissions.nav
DOI: 10.1177/1948550617751584
A Computational Analysis journals.sagepub.com/home/spp
Sudeep Bhatia1, Geoffrey P. Goodwin1, and Lukasz Walasek2
Abstract
We study media representations of Hillary Clinton and Donald Trump in the 2016 U.S. presidential election. In particular, we train
models of semantic memory on a large number of news media outlets that published online articles during the course of the
election. Based on the structure of word co-occurrence in these media outlets, our models learn semantic representations for the
two presidential candidates as well as for widely studied personality traits. We find that models trained on media outlets most
read by Clinton voters and media outlets most read by Trump voters differ in the strength of association between the two
candidates’ names and trait words pertaining to morality. We observe some differences for trait words pertaining to warmth, but
none for trait words pertaining to competence.
Keywords
social perception, semantic representation, political psychology, computational models, media
The relationship between trait associations and the perception representations in media coverage pertaining to a particular elec-
of individuals and groups has a long history of research in psy- tion. The election that we consider is the 2016 U.S. presidential
chological science. Within social psychology, dimensional election, and we attempt to uncover person representations and
models of person perception have been dominant, positing that corresponding trait associations with Donald Trump and Hillary
there are two (e.g., Cuddy, Fiske, & Glick, 2008; Fiske, Cuddy, Clinton present in a large number of media outlets that published
& Glick, 2007; Fiske, Cuddy, Glick, & Xu, 2002) or three (e.g., articles during this election. In addition to helping us character-
Brambilla, Rusconi, Sacchi, & Cherubini, 2011; Brambilla, ize media representations for Trump and Clinton, this approach
Sacchi, Rusconi, Cherubini, & Yzerbyt, 2012; Goodwin, allows us to test for differences between media outlets favored
Piazza, & Rozin, 2014; Landy, Piazza, & Goodwin, 2016; by Trump voters and media outlets favored by Clinton voters.
Leach, Ellemers, & Barreto, 2007) key trait dimensions upon In our approach, we utilize distributional models of semantic
which individuals and social groups are evaluated. In the con- memory and our contribution is therefore to also showcase how
text of political preferences, existing research suggests that the this methodology can be applied to studying person perception
most important trait dimension is competence (Ballew & in the context of geopolitical events.
Todorov, 2007; Cislak & Wojciszke, 2006; Funk, 1997;
Todorov, Mandisodza, Goren, & Hall, 2005), although other
research also highlights the importance of the morality dimen- Trait Associations for Political Candidates
sion (Goodwin et al., 2014). In general, much of the existing work on person perception in
There are two difficulties facing researchers who attempt to the political domain has examined how voter preferences
estimate the dimensional structure of representations for real depend on the target’s trait dimensions (e.g., how competent
political candidates. Firstly, associations between individuals is a specific candidate perceived to be). In this type of research,
and traits or dimensions may vary as a function of political con- participants are often presented, either explicitly or implicitly,
text (e.g., Goodwin et al., 2014; Judd, James-Hawkins, Yzerbyt,
& Kashima, 2005). Second, determining trait associations with
political candidates in the past (i.e., in the context of political 1
Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
2
events that have already occurred) is nearly impossible using University of Warwick, Coventry, West Midlands, United Kingdom
survey-based techniques, making retrospective analyses of per-
Corresponding Author:
son perception in the political domain very difficult. Sudeep Bhatia, Department of Psychology, University of Pennsylvania,
One alternative approach to standard survey-based tech- Philadelphia, PA, USA.
niques, and one that we pursue in the present article, is to study Email: bhatiasu@sas.upenn.edu124 Social Psychological and Personality Science 9(2)
with trait information about different individuals. Less is sources that were read by Trump or Clinton voters. For this pur-
known, however, about the origin of trait–candidate associa- pose, we first ran a pilot study of media consumption
tions, and how these associations are influenced by the imme- preferences.
diate political context. Existing evidence strongly suggests that
news media play an important role shaping people’s voting atti-
Method
tudes and preferences (DellaVigna & Kaplan, 2007; Gerber,
Karlan, & Bergen, 2009; also Margetts, 2017). Media coverage In the first session of this study, we asked 200 U.S. citizens,
is often used as a source of information about social norms, recruited through the online experiments website, Prolific Aca-
which can shift views of individuals and lead to the polarization demic, about the media outlets they visited regularly and
and homophily of attitudes (Bennett, 2012; Slater, 2007). It is trusted for information about politics and current affairs. Parti-
also well known that people exhibit strong confirmation bias in cipants in this study were shown the names of the 250 media
consuming political information and that selective exposure outlets we had in our news corpus (see corpus description in
can drive the emergence of polarization and compartmentaliza- later sections of this article) and were asked to select as many
tion in political thought. Thus, causality is likely to go both outlets as they wished. After this task, participants were asked
ways, with the preferences for selective consumption on one to indicate which of the political candidates they voted for in
side, and growing personalization and selective marketing by the election. Due to the imbalance in Clinton versus Trump vot-
media to meet demands of specific groups of customers on the ers in this session, we ran a second study session, in which we
other (Bakshy, Messing, & Adamic, 2015; Van Aelst, Sheafer, recruited an additional 100 self-reported Republicans and 100
& Stanyer, 2012). More generally, the content of news media self-reported Democrats from Prolific Academic (who hadn’t
both reflects and influences public sentiment and, for that rea- taken part in our first session) and asked them about their media
son, can yield useful insights about the psychological correlates preferences in the same way as described above. Thus, our
of voters’ preferences (De Vreese & Sementko, 2004; Hop- eventual study had a total (both sessions) of N ¼ 400 partici-
mann, Vliegenthart, De Vreese, & Albaek, 2010). pants (mean age ¼ 33.73, SD age ¼ 11.87, 38% female). In
In order to examine representations for Hillary Clinton and total, we had 111 Trump voters and 185 Clinton voters in our
Donald Trump in news media, we adopt the insights of distri- sample.
butional models of semantic memory. These models propose
that people’s semantic representations for objects and concepts Results
can be approximated by examining the statistical structure of
the natural language environments that they are exposed to We pooled the data from these two sessions and used it to cal-
(Griffiths, Steyvers, & Tenenbaum, 2007; Jones & Mewhort, culate the degree to which Clinton and Trump voters relied on
2007; Landauer & Dumais, 1997; Mikolov, Sutskever, Chen, each media outlet for political information. For each media out-
Corrado, & Dean, 2013; Pennington, Socher, & Manning, let i, we calculated rC i and rT i , which is the total number of
2014; see Firth, 1957; Harris, 1954, for early theories). This Clinton voters and Trump voters in our study who stated that
insight has been successfully used to study a range of psycho- they relied on that media outlet. We then used rC i and rT i to cal-
linguistic and cognitive phenomena (see Bullinaria & Levy, culate the extent to which Clinton and Trump voters relied on
2007; Jones, Willits, & Dennis, 2015, for a review). Addition- that outlet relative to each of the other media outlets, to
P P250 T
ally, Bhatia (2017a, 2017b) has applied distributional models obtain RC i ¼ rC i = 250 C T T
j¼1 r j and R i ¼ r i = j¼1 r i . Finally,
of semantic memory, trained on news media, and online ency- we calculated a measure of relative voter reliance,
clopedias, to predict high-level judgments including forecast- RVRi ¼ RC i RT i . Strongly positive values of RVRi indicate
ing, social judgment, and factual judgment. Similarly, that the outlet i was relied on more by Clinton voters than
Holtzman, Schott, Jones, Balota, and Yarkoni (2011) and Trump voters, whereas strong negative values of RVRi indicate
Dehghani, Sagae, Sachdeva, and Gratch (2014) have applied that the outlet was relied on more by Trump voters than Clinton
distributional models to study political bias in media. The abil- voters. The 10 media outlets with the highest and lowest RVR
ity of distributional models to approximate actual human scores are listed in Table 1.
semantic representations suggests that training these models
on news media published during the 2016 U.S. presidential
election could reveal key similarities and differences in repre-
Study of Trait Associations in Media Outlets
sentations and trait associations for the two candidates across For our main study, we obtained online news articles published
different types of media outlets. This could, in turn, shed light by 250 U.S.-based media outlets, over the course of the 2016
on how Trump and Clinton voters represented these two election. We trained semantic memory models individually
candidates over the course of the election. on each of these news outlets. This yielded 250 different mod-
els, each with unique representations and associations for a
very large set of words and phrases. These words and phrases
Pilot Study of News Preferences included Hillary Clinton, Donald Trump, and a large number
A critical component of our analysis involves studying differ- of trait words, and thus for each model (i.e., each media outlet)
ences in trait associations for Trump and Clinton in media we were able to calculate the strength of association betweenBhatia et al. 125
Table 1. The 10 Media Outlets With the Highest and Lowest Read- close to 1 capturing words which are very negatively
ership Among Clinton and Trump Voters. associated.
Media Outlet RVR
Note that since the work of Landauer and Dumais, there
have been a number of technical advancements in uncovering
CNN .027 semantic representations from text (e.g., Mikolov et al.,
Washington Post .021 2013; Pennington et al., 2014). These techniques differ in terms
NPR .021 of how they specify word context, and how they use contextual
The New Yorker .014
similarities in word use in the absence of direct word
The Atlantic .014
CBS Local .009 co-occurrence. We do not expect the key qualitative results
FOX Sports .009 of our analysis to vary with the use of different techniques
NFL.com .010 (though more recent approaches may lead to lower variability
Breitbart News .025 and better quality word representations).
Fox News .047
Note. Readership is quantified using relative voter reliance (RVR) scores, News Corpora
explained in the main text. Positive (negative) values indicate that the outlet
was relied on by Clinton (Trump) voters and not Trump (Clinton) voters. We trained our models on the NOW corpus (http://corpus.
byu.edu/now/), a very large data set of news articles published
the two candidates’ names and the various trait words and online and linked to by Google News. For the purpose of the
dimensions. We then examined both the absolute associations present study, we restricted our analysis to articles published
between the two candidates and trait dimensions, as well as the by U.S. media outlets between June 16, 2015 (the date that
differences in these associations for media outlets predomi- Donald Trump declared his candidacy) and November 7,
nantly relied on by Clinton voters and media outlets relied on 2016 (the day before the election). Our final data set consisted
by Trump voters, based on the ratings from our pilot study. of 322,699 online articles with a total of 82,784,212 words.
To facilitate our analysis, we lowercased all words in the
corpus of news articles, removed all punctuation, and replaced
Method each mention of donald trump and hillary clinton with
donald_trump and hillary_clinton, respectively. We then iden-
Latent Semantic Analysis (LSA) tified unique words based on white space (using donald_trump
The semantic model used in this article is LSA (Landauer & and hillary_clinton ensured that we retained representations for
Dumais, 1997; Landauer, Foltz, & Laham, 1998). The core the full names of the candidates rather than splitting them up
insight of LSA is that words can be represented using multidi- into first and last names).
mensional vectors, which are obtained by performing a dimen-
sionality reduction on word distribution data. The relationship
or association between words is subsequently captured by the
Candidate–Trait Associations
proximity of the vectors for their corresponding words. We trained LSA models on each of the 250 media sources sep-
More formally, for a natural language environment with N arately, yielding 250 models. For each model, we considered
different words occurring in K contexts, we can represent the each article to be a unique context. Thus, for media outlet i,
structure of word distribution using an N K matrix S. S cap- we obtained the matrix Si capturing the occurrence of words
tures word context co-occurrence, so that the number of times across the articles in this outlet. We also applied a term fre-
word n occurs in context k is indicated by the value of the cell quency inverse document frequency (tf idf) transforma-
in row n and column k of S. LSA recovers vector representa- tion to each matrix S i (which is recommended in order to
tions for each of the words by decomposing S into some control for word frequency effects) prior to performing singular
M126 Social Psychological and Personality Science 9(2)
of overall valence for each trait word (how positive or negative Table 2. Interaction Effects Between Relative Voter Reliance on the
each word is), but we did not include these ratings because of Media Outlet and the Dimension Rating of the Trait Word for Predict-
their nonspecificity. ing the Association Between That Trait and Clinton Versus Trump, in
the Media Outlet.
In order to calculate the association between the presidential
candidates and these traits, we used the vectors corresponding Regressions b SE z p 95% CI [L, H]
to the individual trait words (e.g., honest, warm, and intelli-
gent) and calculated their cosine similarity with the vectors cor- Individual regressions
Ability .05 .07 0.70 .49 [0.18, 0.09]
responding to donald_trump and hillary_clinton. Using these
Agency .05 .07 0.63 .53 [0.10, 0.19]
methods, we had, for each outlet and for each trait word, a mea- Character .21 .06 3.33Bhatia et al. 127
Two-dimensional theories posit that these dimensions are Table 3. The Set of High Morality and Low/Neural Warmth, Low/
warmth (which includes morality) and competence, whereas Neutral Morality and High Warmth, and High Competence Words,
three-dimensional theories posit that these dimensions are Used to Isolate the Effects of the Morality, Warmth, and Competence,
on Trait Ratings.
warmth, morality, and competence. In order to examine trait
associations in terms of these core dimensions, we generated High Morality and Low/ Low/Neutral and Morality High
a composite variable for morality/warmth by averaging ratings Neutral Warmth High Warmth Competence
for the character, communion, goodness, morality, and warmth
Courageous Warm Athletic
dimensions, a composite variable for morality by averaging rat-
Fair Sociable Musical
ings for the character, goodness, and morality dimensions, and Principled Happy Creative
a composite variable for competence by averaging the ratings Responsible Agreeable Innovative
for ability and agency dimensions, for each trait. We then ran Just Enthusiastic Intelligent
individual regressions testing for the effect of our composite Honest Easygoing Organized
dimensions on trait associations. Three new regressions Trustworthy Funny Logical
revealed that both the morality/warmth composite and the mor- Loyal Playful Clever
ality composite had strong significant interaction effects with
the relative voter reliance on the media outlet (p < .001; see
Table 2 for details) that survive the Holm–Bonferroni correc- still failed the Holm–Bonferroni correction. We still found a sig-
tion for multiple comparisons. In contrast, the competence nificant interaction effect for the morality/warmth composite
composite did not display this interaction effect. (p < .01), in a regression with the morality/warmth composite
We also ran variants of the above regressions directly com- and the competence composite. The significant interaction
paring the composite dimensions against each other. In the first effect of a separate morality composite in a variant of this regres-
combined regression, we included the morality/warmth and sion including warmth and the competence composite also per-
competence composite. In our second combined regression, sisted (p < .05). A related set of regressions for only negatively
we used three composites, for warmth, morality, and compe- valenced words (words below the median valence on Goodwin
tence. The former model revealed a strong interaction effect for et al.’s trait ratings) did not yield any significant differences for
the morality/warmth composite (p < .001), but not the compe- any dimensions of interest (p > .05). This result could be due to
tence composite. The latter model revealed a significant posi- differences in frequency of word use for positively and nega-
tive interaction effect for the morality composite (p < .05), tively valenced traits, in news media.
but not the warmth dimension or the competence composite.
Overall, these results indicate that media outlets read by
Clinton (Trump) voters were more likely to associate Clinton
Dimension Independence Analysis
(Trump) with traits rated highly on the morality composite. Results reported so far suggest that semantic representations of
These is no evidence for differences on the warmth or compe- Clinton and Trump present in media read by Clinton and
tence composites. The results of these two regressions are also Trump voters varied primarily in terms of morality associa-
summarized in Table 2. Again, there were no significant main tions. There seems to be some evidence that warmth associa-
effects of the dimensional composites on relative associations. tions could have varied as well. However, the warmth
dimension is positively correlated with the morality dimension
(as well as the morality composite), so this relationship could
Valence Analysis be incidental. Our analysis testing for the independent effects
Goodwin et al.’s (2014) data set contains negative trait words of both the morality composite and warmth simultaneously
that occasionally map on quite strongly to dimensions like mor- found no effect of warmth (but a significant positive effect of
ality and warmth (e.g., dishonest has a high rating on the mor- morality).
ality dimension as it is strongly reflective of the morality of an Another way to attempt this analysis is to use only a subset
individual). It is necessary to rerun the above analysis exclud- of the trait words that Goodwin et al. compiled that are either
ing the negatively valenced words, to ensure that the effects high in morality and neutral/low in warmth or neutral/low in
documented above did not emerge due to a perverse association morality and high in warmth. There are eight such high moral-
between Clinton and immorality-related words in pro-Clinton ity/low warmth words and eight low morality/high warmth
outlets, and Trump and immorality-related words in pro- words in Goodwin et al.’s Study 3, and we attempted to repli-
Trump outlets. cate our above analysis using these words. For comparability,
We did this by performing a median split on the valence rat- we also performed this analysis with the eight high competence
ings in Goodwin et al.’s data and excluding trait words below the words in Goodwin et al. The words used in this analysis are
median valence. This regression did not change findings for the summarized in Table 3.
nine dimensions, except that the interaction effect of character First, we ran three separate regressions, examining the indi-
decreased in significance to p ¼ .02, failing the Holm–Bonfer- vidual effects of these sets of words on trait associations. In the
roni correction for multiple comparisons. The interaction effect first regression, our dependent variable was relative association
of warmth also increased in significance to p ¼ .01; however, it RAij, and the independent variables were the relative voter128 Social Psychological and Personality Science 9(2)
Table 4. Interaction Effects Between Relative Voter Reliance on the possessed by these models between Hillary Clinton, Donald
Media Outlet and the Dimension Rating of the Trait Word for Predict- Trump, and various words describing personal traits. Compar-
ing the Association Between That Trait and Clinton Versus Trump in ing media outlets favored by Clinton voters against media out-
the Media Outlet.
lets favored by Trump voters, we found that differences in
Regressions b SE z p 95% CI [L, H] representations pertained primarily to morality, with Clinton
outlets more strongly associating Clinton with moral traits rela-
Individual regressions tive to Trump, and Trump outlets more strongly associating
Morality .84 .35 2.39Bhatia et al. 129
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Declaration of Conflicting Interests Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of
social cognition: Warmth and competence. Trends in Cognitive
The author(s) declared no potential conflicts of interest with respect to
Sciences, 11, 77–83.
the research, authorship, and/or publication of this article.
Fiske, S. T., Cuddy, A. J., Glick, P., & Xu, J. (2002). A model of (often
mixed) stereotype content: Competence and warmth respectively
Funding
follow from perceived status and competition. Journal of Person-
The author(s) disclosed receipt of the following financial support for ality and Social Psychology, 82, 878.
the research, authorship, and/or publication of this article: Funding for Funk, C. L. (1997). Implications of political expertise in candidate
Sudeep Bhatia was received from the National Science Foundation trait evaluations. Political Research Quarterly, 50, 675–697.
Grant SES-1626825. Funding for Lukasz Walasek was received from Gerber, A. S., Karlan, D., & Bergan, D. (2009). Does the media mat-
Leverhulme Trust Grant RP2012-V-022. ter? A field experiment measuring the effect of newspapers on vot-
ing behavior and political opinions. American Economic Journal:
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wick with interests in social and economic behavior.
Leach, C. W., Ellemers, N., & Barreto, M. (2007). Group virtue: The
importance of morality (vs. competence and sociability) in the Handling Editor: Jesse GrahamYou can also read
