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Journal of Communication ISSN 0021-9916
ORIGINAL ARTICLE
Same Candidates, Different Faces:
Uncovering Media Bias in Visual Portrayals of
Presidential Candidates with Computer
Vision
Yilang Peng
Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104, USA
How do today’s partisan media outlets produce ideological bias in their visual coverage
of political candidates? Applying computer vision techniques, this study examined
13,026 images from 15 news websites about the two candidates in the 2016 U.S. presi-
dential election. The analysis unveils a set of visual attributes (e.g., facial expressions,
face size, skin condition) that were adopted by media outlets of varying ideologies to dif-
ferentially portray these two candidates. In addition, this study recruited 596 crowd-
sourced workers to rate a subset of 1,200 images and demonstrated that some visual
features also effectively shape viewers’perceptions of media slant and impressions of the
candidates. For example, Clinton was portrayed with more expressions of happiness,
which rendered her as more favorable, whereas Trump was associated with more
expressions of anger, which made him look less positive but more dominant. These dif-
ferences in facial expressions varied in line with media outlets’political leanings.
Keywords: Media Bias, Visual Bias, Face Perception, Trait Perception, Non-Verbal
Communication, Computer Vision, Computational Social Science, Crowdsourcing.
doi:10.1093/joc/jqy041
Increasingly, media outlets are explicitly labeling their political affiliations to market
themselves (Groeling, 2013). In the hope of exposing media consumers to more bal-
anced and diverse viewpoints, scholars have made various attempts to automatically
label political slants in media content (Park, Kang, Chung, & Song, 2009). Given the
difficulty in analyzing images on a large scale, prior research examining content bias
across media outlets has been mostly limited to textual data (Groeling, 2013). Yet,
visual content proliferates in the digital media environment and is widely used in
political communication (Grabe & Bucy, 2009;Verser & Wicks, 2006). Empirical
studies also demonstrate that visual attributes such as facial expressions and face
Corresponding author: Yilang Peng; e-mail: yilang.peng@asc.upenn.edu
1Journal of Communication 00 (2018) 1–22 © The Author(s) 2018. Published by Oxford University Press on behalf of
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size can effectively shape our impressions and voting preferences towards politicians
(Mutz, 2007;Tiedens, 2001). This research gap regarding visual bias should require
special attention today.
This study tackled the challenge of analyzing large-scale visual media by bridg-
ing communication research and computer vision, a field that trains computers to
understand digital imagery (Szeliski, 2010). Applying computer vision techniques
such as facial detection and emotional analysis, this study examined 13,026 images
from 15 news websites covering the 2016 U.S. presidential candidates—Hillary
Clinton and Donald Trump—regarding various visual features. Beyond this meth-
odological contribution, this study also advances our understanding of visual bias in
the following ways. First, while extensive research has been devoted to quantifying
the direction and magnitude of partisan bias, relatively limited studies have exam-
ined the forms of bias in visual content. Based on the ideological positions of media
outlets established by prior research (Budak, Goel, & Rao, 2016;Flaxman, Goel, &
Rao, 2016;Mitchell, Gottfried, Kiley, & Matsa, 2014), this study reveals the specific
portrayals that media outlets use to convey their partisan views about the two candi-
dates. In addition, this research also recruited crowdsourced workers to rate a sub-
sample of images on their perceptions of media slant and impressions of the two
candidates. By doing so, this study further illuminates how different visual represen-
tations adopted by partisan media potentially affect audiences of varying ideologies.
Measuring partisan media bias in visual content
This study defines partisan bias as systematic patterns in media content that favor
one political party, candidate, or ideology over another (Groeling, 2013;Waldman
& Devitt, 1998). First, bias should be distinguished from slant in a specific piece of
media content, as bias should be “systematic, rather than anecdotal, episodic, or
fleeting”(Groeling, 2013, p. 133). In addition, as Entman (2007, p. 166) argued, to
establish media bias, researchers need to show “patterns of slant that regularly
prime audiences, consciously or unconsciously, to support the interests of particular
holders or seekers of political power.”Our operationalization of bias thus retires the
notion that it is a deviation from the truth but instead incorporates its potential
impact on audiences. If a piece of content is determined as favoring certain political
actors, it is reasonable to expect that viewers exposed to it should form favorable
impressions about the targets or at least perceive the intended slant. While prior
research has proposed other types of partisan bias, such as coverage bias (which
focuses on the volume of coverage), this study focused on presentation bias, which
specifically deals with the favorability of media coverage toward one party or ideol-
ogy over the other (D’Alessio & Allen, 2000;Groeling, 2013).
Previous attempts at measuring visual slant can be broadly categorized into two
approaches. Some have used more objective measures by coding visual content on a
checklist of features that researchers predefine as (un)favorable treatment of politi-
cians. The criteria used to determine visual slant usually include a politician’s
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nonverbal behaviors (e.g., facial expressions, hand gestures, activity), contextual fea-
tures (e.g., photographic settings, other objects, and people in the same picture),
and structural features (e.g., camera angle, color; Grabe & Bucy, 2009;Moriarty &
Popovich, 1991;Verser & Wicks, 2006;Waldman & Devitt, 1998). This checklist
approach offers nuanced understandings of how bias is embodied in specific visual
portrayals, although, as scholars have noted, different visual features do not contrib-
ute equally to the favorability of images (Barrett & Barrington, 2005). It also
remains unknown whether these attributes, selected by scholars, indeed influence
audience interpretations (Lobinger & Brantner, 2015).
Others have used more subjective measures by instructing coders to rate the
favorability of media content. For example, in Barrett and Barrington (2005), three
coders rated photos of politicians on a “highly unfavorable”to “highly favorable”
scale. In Hehman, Graber, Hoffman, and Gaertner (2012), six coders evaluated can-
didates in photos on warmth and competence. With crowdsourcing platforms like
Amazon Mechanical Turk, scholars can recruit more coders from diverse demo-
graphic and ideological backgrounds to assess bias on a large scale (Budak et al.,
2016). Nevertheless, this approach still relies on coders’subjective interpretations
and answers only how much, but not how, media content is biased. As Grabe and
Bucy (2009, p. 101) argued, “visual analyses should move beyond the ‘positive ver-
sus negative’index measures and investigate more specific and nuanced character
frame–building dimensions.”
These two approaches can be regarded as not only different methods for quanti-
fying bias, but also two routes of conceptualizing bias that complement each other.
Partisan bias should first be established as systematic patterns of differential treat-
ment of political actors in media content. However, differences alone do not guaran-
tee favorability; it requires additional efforts to demonstrate that these patterns
indeed (dis)advantage certain actors among audiences. This study thus integrated a
content analysis that investigated whether the visual coverage of candidates does
vary by media outlets with a survey that asked crowdsourced workers to rate the
favorability of these pictures. By doing so, we hoped this research would reveal
visual cues that (a) reveal media outlets’ideological positions and (b) influence
audience perceptions of favorability.
Selection of visual features
The first step in our approach was to select visual features that could be captured by
computer vision applications on a large scale and that should, theoretically, reflect
media bias. Based on previous scholarship on visual bias, social cognition, and polit-
ical psychology, this study focused on the following features:
Facial orientation
Current facial detection algorithms often show facial orientation in three angles:
pitch refers to the extent of a head bowing down or raising up; roll reflects the
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extent of a head tilting to the left or to the right; and yaw shows the extent of a face
turning to the left or to the right (Figure 1). The pitch angle can be seen as a proxy
of camera angle, a criterion frequently used in prior research to indicate visual
favorability. A low camera angle (face pitching upward) is regarded as a better por-
trayal, as it could convey a sense of dominance and power compared with a high
angle (face pitching downward; Grabe & Bucy, 2009;Waldman & Devitt, 1998). It
is unclear, however, if the other two angles—roll and yaw—are related to visual
bias.
Face size and location
As a cue for judging interpersonal distance, face size might pose mixed impacts on
perceptions of other people. On one hand, compared with a long shot, a close-up
portrait makes a person seem closer to viewers, thus appearing more intimate or
dominant (in the sense of face-to-face confrontation; Archer, Iritani, Kimes, &
Barrios, 1983;Grabe & Bucy, 2009). On the other hand, an extreme close-up might
be a negative portrayal, as it resembles an extremely close physical distance that vio-
lates the notion of personal space. It also brings a person’s face under detailed scru-
tiny, revealing skin flaws or awkward expressions (Grabe & Bucy, 2009). In
addition, prior research has also claimed that featuring a candidate dominating the
photo or as the center of attention positively portrays the candidate (Verser &
Wicks, 2006). A photo that locates a politician’s face closer to the center should cast
the person in a better light.
Facial expressions
Facial expressions of emotion—motions or positions of facial muscles that convey
the emotional state (Ekman & Friesen, 2003)—have also been used to evaluate
visual bias. Looking happy or confident is usually coded as a positive representation
of politicians, while frowning or looking sad, worried, or tired is seen as negative
(Moriarty & Popovich, 1991;Waldman & Devitt, 1998). Happy faces are also per-
ceived as more trustworthy, attractive, and dominant (Knutson, 1996;Oosterhof &
Todorov, 2008;Sutherland et al., 2013). Besides coding facial expressions on a posi-
tive–negative spectrum, scholars have also argued for distinctions among discrete
emotions. Ekman and Friesen (2003) identified six basic facial expressions: happi-
ness, sadness, fear, anger, surprise, and disgust. Grabe and Bucy (2009) also distin-
guished among anger/threat, fear/evasion, and happiness/reassurance. Different
negative emotions may produce distinct impressions: for example, individuals
showing anger or disgust are often perceived as more dominant and powerful than
those showing sadness or fear (Knutson, 1996).
Eye and mouth status
Eye status is another criterion frequently used to evaluate the favorability of photo-
graphs. Looking directly at the camera or at someone in pictures is coded as a posi-
tive portrayal of the candidate, whereas closed eyes portray a politician negatively
(Moriarty & Popovich, 1991;Verser & Wicks, 2006). Research has shown that eye
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openness could make people look more intelligent and attractive (Talamas, Mavor,
Axelsson, Sundelin, & Perrett, 2016). Mouth openness should also be an indicator
of favorability. Prior research has regarded exhibiting dynamic behaviors such as
speaking as a favorable depiction of politicians (Verser & Wicks, 2006). Mouth
openness also reflects the intensity of smiling, although it can also indicate yelling
and shouting that convey aggressiveness.
Skin condition
Skin condition has rarely been examined in visual bias literature, but its effects are
frequently documented in face perception research. First, darkening a face’s skin
tone often leads to more negative reactions to it (Alter, Stern, Granot, & Balcetis,
2016;Ronquillo et al., 2007). An analysis of news articles showed that how posi-
tively an article portrayed a person correlated with the person’s skin lightness in the
article’s visuals (Alter et al., 2016). Facial skin coloration is another important cue
in impression formation. Skin redness and yellowness can be used as cues for infer-
ring health status (Stephen, Smith, Stirrat, & Perrett, 2009). Signaling increased
blood flow, facial redness is also linked to perceptions of attractiveness, dominance,
and aggressiveness (Stephen, Oldham, Perrett, & Barton, 2012). Lastly, faces with
healthier skin are often rated as more attractive, whereas skin imperfections such as
wrinkles and uneven pigmentation make a person look older, less healthy, and less
attractive (Fink, Grammer, & Matts, 2006;Jones, Little, Burt, & Perrett, 2004).
Other people
In prior research, presenting a cheering crowd or attentive colleagues together with
a politician has often been coded as a positive representation, whereas featuring the
politician alone or with inattentive crowds or colleagues has been seen as negative
(Moriarty & Popovich, 1991;Verser & Wicks, 2006). Being accompanied by other
Figure 1 Facial orientation in pitch, roll, and yaw angles. Credit: Kyle Cassidy.
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people in pictures would also make a person appear more attractive than being
photographed alone (Walker & Vul, 2014). Therefore, the presence of other people
in pictures, as well as their facial expressions and eye openness, can also be proxies
of media bias.
Visual bias across liberal and conservative media outlets
Having proposed a list of computer vision features that should theoretically reflect
media bias, this study then asked how bias would be embodied in differential por-
trayals of politicians. Past research on partisan bias often focused on whether the
media deviates from the norm of balance and exhibits an overall liberal or conserva-
tive bias, but no consistent patterns have emerged. For example, regarding television
news, D’Alessio and Allen’s (2000) meta-analysis revealed a detectable but small
pro-Democrat bias, whereas Grabe and Bucy (2009) found a persistent pro-
Republican one. Given the rise of partisan media outlets that explicitly favor one
side instead of sticking to the norms of objectivity and balance, scholarship has
gradually shifted to quantifying bias at the individual media outlet level (Budak
et al., 2016). Regarding visual bias, research has shown that media outlets like news-
papers (Barrett & Barrington, 2005) and websites (Hehman et al., 2012) indeed pub-
lish photos that portray candidates they endorse more favorably than the
candidates’opponents. Therefore, we should expect that liberal media portrayed
Clinton better than Trump and conservative media acted reversely prior to the elec-
tion. However, it remains unknown what visual cues are adopted by media outlets
of various political affiliations in their (un)favorable treatment of politicians, which
is one focus of this study.
The analysis of partisan bias was further complicated in this case because the
past election had both a female and a male candidate. Although this research can
uncover differences in visual representations of Trump and Clinton, it is difficult to
attribute these differences solely to partisan bias or gender bias, as some attributes
related to favorability are also linked to gender. For example, faces are often shown
as more prominent in visual depictions of men than those of women, a phenome-
non termed as “face-ism”in prior research (Archer et al., 1983). Gender is also ste-
reotypically linked to different emotions: anger is often seen as more masculine,
signifying aggressiveness and dominance, whereas happiness, sadness, and fear are
seen as more feminine, showing friendliness or weakness (Plant, Hyde, Keltner, &
Devine, 2000). Therefore, instead of focusing on the overall contrast between
Clinton and Trump, this study asked whether the differential treatment between the
two candidates varied by media outlets’ideological positions, thus uncovering visual
cues that signalled their political orientation.
RQ1: Which visual features—facial orientation (pitch, roll, yaw angles), face size,
face location, facial expressions (e.g., happiness, anger, sadness), eye openness, eye
gaze direction, mouth openness, facial skin condition (lightness, redness,
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yellowness, health), presence of other people, other people’s facial expressions, and
eye openness—were used by liberal and conservative media to differently portray
Clinton and Trump?
Favorability and its dimensions
As noted earlier, the notion of favorability in media bias implies that biased content
should indeed advantage certain political actors among media consumers. One
important function of visual media in political communication is to convey cues
that help us judge politicians’traits and characters (e.g., warmth, competence;
Lobinger & Brantner, 2016), which in turn could influence voting preferences
(Caprara & Zimbardo, 2004). Nevertheless, the majority of media bias studies often
see bias as a single unfavorable-versus-favorable spectrum, which might not capture
the diverse effects of visual portrayals on viewers’impressions. For example, show-
ing the negative emotion of anger might make a person look unfriendly, but simul-
taneously dominant (Knutson, 1996;Tiedens, 2001). In addition, different traits
also correspond to gender stereotypes: women are expected to be more friendly and
kind, whereas men are perceived to be more assertive and aggressive (Prentice &
Carranza, 2002). This study thus asked whether different visual representations of
politicians actually affect viewers’perceptions of media slant, as well as evaluations
of politicians, on separate trait dimensions.
Prior research is still divided on what specific dimensions govern our judgment
of people from visual portrayals. Research in person perception claims that two fun-
damental dimensions underlie our judgments of people: one dimension (commu-
nion) captures traits related to perceived intent and could be further divided into
two sub-dimensions, warmth (e.g., sociable, friendly) and morality (e.g., trustwor-
thy, sincere); and the other dimension (agency) captures traits about perceived abil-
ity and incorporates two sub-dimensions as well, competence (e.g., intelligent,
competent) and dominance (e.g., dominant, assertive; Abele et al., 2016). Evaluating
politicians could be seen as a specific case of person perception. Caprara and
Zimbardo (2004) also found a two-factor structure in judging personalities of politi-
cians—energy and agreeableness—which largely overlap agency and communion.
Research in face perception also proposes that we use multiple dimensions to
infer traits from human faces. The first dimension, labeled as valence, incorporates
traits related to warmth, morality, and competence, indicating an overall favorabil-
ity in impressions. The second dimension reflects only dominance (Oosterhof &
Todorov, 2008). Attractiveness has also been proposed as a third factor in perceiving
human faces (Sutherland et al., 2013). In this study, news photographs of politicians
include not only their faces, but also social information such as their interactions
with other people. Therefore, this study first examined the structure underlying view-
ers’perceptions of candidates in images and then investigated whether visual features
influence these dimensions differently. Here, this study proposes two research ques-
tions regarding the potential effects of various visual features.
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RQ2: Among the visual features proposed in RQ1, what features could best predict
viewers’judgment of media slant in images of the two candidates?
RQ3: (a) Among warmth, morality, competence, dominance, and attractiveness,
what dimensions underlie people’s perceptions of candidates in news photographs?
(b) And what visual features proposed in RQ1 could best predict audience
perceptions of these dimensions?
Method
Data preparation
Prior research has already placed a list of popular news websites on the liberal–con-
servative spectrum. This research combined insights from several recent studies:
one that averaged crowdsourced workers’perceived slant of each media outlet’s
news articles (Budak et al., 2016) and two using aggregated political orientation of
each outlet’s audience as a proxy of its ideological position (Flaxman et al., 2016;
Mitchell et al., 2014). The sample included eight liberal sites (Daily Kos, Slate,The
New York Times,The Huffington Post,The Washington Post, MSNBC, BBC, CNN),
four relatively neutral sites (USA Today, Reuters, NBC News, The Wall Street
Journal), and three conservative sites (Fox News, Breitbart, and TheBlaze). With
Google search, the study searched for images of the two candidates limited to a spe-
cific news site (e.g., “Hillary Clinton site:cnn.com”). A total of 20,702 still images
were retrieved in the last week of November 2016.
1
All images were transformed to
JPEG format and large images were resized so both width and height did not exceed
1000 pixels.
With a computer vision service, Face++, this study then identified images with
visible faces of the two candidates. The analysis first prepared a face set that
included the two candidates. Next, for each image, the analysis detected whether
this image contained faces (facial detection). For each detected face, Face++ com-
pared this face to faces in the face set and returned the most similar-looking face,
along with a confidence score of the two faces belonging to the same person (facial
recognition).
2
Pictures without faces of Clinton or Trump were excluded. Based on
the URLs of images returned by Google, this study then determined the date of each
photo. The majority of URLs embedded the date when an article or a photo was
published. For remaining images, a combination of web scraping and manual
checking was used. Images from 2015–2016 (N=13,026; 6,543 for Clinton) were
kept in further analysis (Figure 2).
Participant ratings
From Amazon Mechanical Turk, 596 U.S. crowdsourced workers who had com-
pleted at least 100 tasks with an approval rate above 98% were recruited to rate a
subset of 1,200 images randomly selected from the sample (40 for each candidate
and each outlet). Each participant rated a random set of 20 images for each candi-
date. On average, each image received 19.9 ratings (SD =1.66, range =18–32).
3
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Ratings of each image were averaged across participants and used in the later analy-
sis (Oosterhof & Todorov, 2008).
Each image was rated on the following questions.
1. Perceived slant: on five-point scales, participants rated how an image negatively
or positively (1 =extremely negative, 5 =extremely positive) and unfavorably or
favorably (1 =extremely unfavorable, 5 =extremely favorable) portrayed a can-
didate (α=.99; M
Clinton
=3.32, SD =.47; M
Trump
=2.84, SD =.58).
2. Traits perceptions: on five-point scales (1 =strongly disagree; 5 =strongly
agree), participants rated in each image whether the candidate looked friendly,
warm, honest, trustworthy, dominant, assertive, competent, intelligent, attractive,
youthful, and healthy. These adjectives correspond to various constructs pro-
posed in prior research (Table 3;Abele et al., 2016;Oosterhof & Todorov, 2008;
Sutherland et al., 2013).
Figure 2 Data preparation procedures.
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3. Perceived facial expression: given the subjectivity in human perceptions of facial
expressions, this study also asked 396 participants to rate a subsample of 800
images to determine the extent to which the candidates’faces displayed anger,
disgust, fear, happiness, sadness, surprise, and emotional neutrality (1 =not at
all,5=to the full extent). These ratings were used to validate emotional analysis
results from computer vision services.
Computer vision analysis
For each image, Face++ provided the yaw, roll, and pitch angles that represented
facial orientation. Face++ also provided each face’s location in the picture as a facial
rectangle that bounded the face region (Figure 2). Face size was calculated as the
ratio between the size of the face rectangle and the size of the image. Face location
was calculated as 1–2×d
1
/d
2
, in which d
1
was the distance between the face rectan-
gle’s center and the picture’s center, and d
2
was the length of the picture’s diagonal.
A higher value indicated that the face was closer to the center. Face++ also provided
the extent of eye openness and mouth openness, as well as eye gaze direction, as a
three-dimensional vector. The analysis calculated the angle between the gaze direc-
tion and the image, so a higher value approaching 90 degrees would indicate the eye
was looking towards the camera rather than looking elsewhere. Values for eye open-
ness and eye gaze direction were averaged across both eyes.
Regarding facial expressions, prior research has shown that computer vision
tools can accurately detect happiness, but might not identify other facial expressions
well (Dehghan, Ortiz, Shu, & Masood, 2017). The analysis thus compared emotions
Table 3 Principal Component Analysis of Perceived Traits
All
(N=1200) Clinton (600) Trump (600)
Constructs Items 121212
Communion (warmth) Friendly (.89) .87 −.38 .85 −.44 .82 −.43
Warm (.88) .89 −.35 .87 −.41 .86 −.40
Communion (morality) Honest (.65) .95 −.01 .94 −.01 .94 −.05
Trustworthy (.69) .96 −.01 .95 −.02 .94 −.03
Agency (dominance) Dominant (.64) .29 .92 .29 .91 .47 .84
Assertive (.67) .35 .89 .35 .89 .45 .84
Agency (competence) Competent (.73) .92 .24 .88 .35 .92 .20
Intelligent (.74) .90 .22 .85 .38 .91 .19
Attractiveness Attractive (.78) .94 −.12 .93 −.15 .91 −.13
Youthful (.78) .85 −.24 .85 −.28 .79 −.28
Healthy (.73) .93 −.02 .92 −.07 .90 −.02
Variance explained 66.5% 22.4% 62.2% 26.6% 61.1% 25.1%
Note: Inter-rater reliability for each item is provided in brackets. Factor loadings larger than
.5 are shown in bold.
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detected by four popular emotion analysis services—Microsoft, Face++, Sighthound,
and Google Vision—with participants’perceived emotions (Table 1). All services
detected happiness well, with Microsoft performing best (r=.85). Microsoft also pre-
dicted anger (r=.54) and emotional neutrality (r=.63) relatively accurately. Yet, the
detection of other emotions, such as fear and sadness, was not satisfactory. Therefore,
scores of anger, happiness, and emotional neutrality from Microsoft were used and
otheremotionswereexcludedfromtheanalysis. Where Microsoftfailedtodetecta
candidate’s face in a picture, Face++’sresultswereused.
Face++ provided facial landmarks of each face, which were locations of impor-
tant face components, such as face contour, eyes, and mouth (Figure 2). Based on
facial landmarks, the analysis then identified the facial skin region as the facial
region excluding eyebrows, eyes, nose, and mouth. The image was transformed into
the CIELab color space. Lightness, redness, and yellowness of facial skin were calcu-
lated as the average L, a, and b values, respectively, of pixels inside the facial skin
region (Stephen et al., 2012). In addition, Face++ also returned likelihoods about a
face’s skin condition. Skin health was calculated as the difference between the likeli-
hood that the skin was healthy and average likelihood that the skin showed different
types of ill conditions, such as dark circles and stains.
4
The number of other people’s faces detected by Face++ was used to indicate
whether a candidate was presented with other people or not. If the candidate was
accompanied by multiple individuals in an image, the means of other faces’happi-
ness and eye openness were used as predictors. For a small portion of images with
multiple faces of the same candidate (e.g., Trump standing in front of a screen
showing his face; .9%), face-related visual attributes were averaged across the candi-
date’s faces. The number of repetitions of the candidate’s face was included as a
control variable. A few computationally-calculated aesthetical features were also
included as control variables, including brightness and contrast, measured as the
mean and the standard deviation of all pixels’perceived luminance values; colorfulness,
basedonthecombinationofR,G,andBpixelsintheRGBcolorspace(
Peng &
Jemmott, 2018); and image size and aspect ratio, measured as the product and the quo-
tient of an image’s width and height.
Results
The study first examined whether the treatment between Clinton and Trump dif-
fered between liberal and conservative media regarding various visual features
(RQ1). The analysis constructed a candidate variable (0 =Clinton, 1 =Trump) and
a media outlet variable (1 =liberal media, 2 =relatively neutral, 3 =conservative
media) based on the ideological positions of media outlets, as quantified by prior
research (see Method section). A series of moderated multiple regressions were con-
ducted (Jaccard & Turrisi, 2003), with each regression using the candidate variable,
the media outlet variable, and their interaction to predict one visual feature
(Table 2). For each visual feature, a statistically-significant coefficient of the
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Table 1 Correlations Between Human-Perceived and Computer Vision Services–Detected
Facial Expressions
Face++ Microsoft Sighthound Google Vision
Anger (.88) .29*** .54*** .31*** .23***
Disgust (.81) .25*** .36*** .09* NA
Fear (.50) .08* .19*** .11** NA
Happiness (.96) .79*** .85*** .71*** .80***
Sadness (.64) .04 .28*** .18*** .17***
Surprise (.77) .24*** .39*** .23*** .36***
Neutral (.74) .46*** .67*** .36*** NA
N791 738 783 772
Note: Analyses were performed on images containing only one face of the candidate (N=
791). The sample size varied, as some faces detected by Face++ were not detected by other
services. NA =not available. The inter-rater reliability for each item is provided in brackets.
*p<.05, **p<.01, ***p<.001.
Table 2 Distribution of Visual Features Across Candidates and Media Outlets
Candidate Media Outlet Candidate ×Media Outlet
Face orientation: Pitch .14*** −.01 .02
Roll −.11*** .02* −.02
Yaw −.02 .01 −.01
Face size .14*** .06*** −.12***
Face location −.09*** .18*** −.02
Facial expressions: Happiness −.46*** −.06*** .08***
Anger .42*** .02
†
−.06**
Neutral .09*** .01 .02
Eye openness −.30*** −.01 .04
†
Eye gaze −.22*** .03* −.01
Mouth openness −.11*** .00 −.03
Skin condition: Skin lightness −.19*** .03** −.04
†
Skin redness .40*** .01 .02
Skin yellowness .08*** .04** .02
Skin health −.09*** −.05*** .07**
Number of other people’s faces −.12*** −.03** .07**
Other faces’happiness −.04 −.01 .02
Other faces’eye openness .13*** .02 .00
Note:N=13026 (N=4939 for other faces’happiness and eye openness). Candidate: 0 =
Clinton, 1 =Trump. Media outlet: 1 =liberal, 2 =neutral, 3 =conservative. Each line repre-
sents one regression model. Standardized coefficients are shown.
†
p<.10, *p<.05,
**p<.01, ***p<.001.
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candidate variable would imply that media overall portrayed the two candidates dif-
ferently regarding that feature. As noted earlier, this difference could be a mix of
partisan and gender biases, or characteristics of these two candidates (e.g., Trump
and Clinton differ in their skin tones). A significant interaction would imply that
the size of this difference was moderated by media outlets’political orientations,
suggesting the presence of partisan bias.
In overall media coverage, compared with Clinton, Trump images had larger
faces (β=.14), showed less happiness (β=−.46) but more anger (β=.42), and por-
trayed less healthy facial skin (β=−.09) and fewer other people’s faces (β=−.12,
all ps<.001). As indicated by significant interactions, these gaps regarding face size
(β=−.12, p<.001), happiness (β=.08, p<.001), anger (β=−.06, p=.002), skin
health (β=.07, p=.002), and number of other faces (β=.07, p=.001) narrowed
or reversed as the media outlets’political orientations moved from liberal to conser-
vative (Figure 3), implying that these attributes were adopted by outlets to differen-
tially portray the two candidates. For example, regarding happiness, the gap between
Clinton and Trump was 32 overall (on a 1–100 scale), and was wider in liberal media
(34) than in conservative media (27). The gap was most pronounced in Daily Kos (46)
and least in Breitbart (23), two sites situated at two extreme ends of the ideological
spectrum. Face size served as another example. While liberal and relatively neutral
outlets almost universally portrayed Trump images with larger faces than Clinton, the
gap diminished or reversed in conservative sites such as Fox News, TheBlaze, and
Breitbart. A similar pattern occurred for skin health. With two outliers (The New York
Times and The Huffington Post), liberal media, especially Daily Kos and Slate, por-
trayed Clinton with healthier facial skin, but this gap narrowed in conservative media.
Having shown what visual features were adopted by media outlets of varying
positions as signals of their political leanings, the analysis then investigated whether
these features indeed impacted viewers’perceptions (RQ2). The inter-rater reliabil-
ity (IRR) of participants’ratings was calculated based on intraclass correlation coef-
ficients (see Kim, 2014, p. 167). The IRR for perceived slant was quite high (.87),
suggesting a high degree of agreement among participants. An ordinary least
squares regression used computer vision features to predict averaged perceived
slant, controlled for which candidate an image featured (Figure 4a). Given that
raters were randomly assigned to a large pool of images, it was unlikely that the
raters’characteristics would be exactly the same across all the images. The model
thus controlled for the number of raters assigned to each image and the aggregated
characteristics of each image’s raters, including percentages of raters who were women
and White and means of raters’ages, education levels, and political orientations.
Detected emotional neutrality highly correlated with happiness (r=−.71, p<.001),
and was therefore removed from the model. All variance inflation factors were below 3.
Among all the attributes, expressions of happiness had the largest effect size
(β=.48). Images with large face sizes (β=−.19) and expressions of anger (β=
−.15) were rated as negative portrayals of candidates, whereas skin health (β=.09;
all ps<.001) positively contributed to favorability. These results showed a large
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overlap between visual features that differentiated liberal and conservative media and
features that influenced audience perceptions of favorability. Based on unstandardized
coefficients, a completely happy face, a completely angry face, and a face with per-
fectly healthy skin would impact a picture’s favorability by +.8, −.7, and +.4, respec-
tively, on a 5-point scale. In addition, mouth openness (β=.10, p<.001) and other
faces’happiness (β=.07, p=.002) also slightly enhanced perceived favorability.
Next, this study looked at the dimensionality in the evaluation of traits from
photographs (RQ3a). The IRR in perceiving different traits ranged from .64 to .89,
indicating reasonably high agreement among participants, particularly for traits
related to warmth, competence, and attractiveness, but less for dominance and
morality (Table 3). A two-factor structure emerged from exploratory factor analy-
ses. Two terms related to dominance (dominant and assertive) loaded on one factor
(IRR =.68; α=.95; M
Clinton
=3.25, SD =.45; M
Trump
=3.25, SD =.46). Warmth,
morality, competence, and attractiveness did not form distinct concepts, as some
prior research had implied; instead, these traits converged on another factor,
referred to as valence (IRR =.77; α=.98; M
Clinton
=3.09, SD =.51; M
Trump
=2.57,
SD =.41; Table 3).
The analysis then looked at what visual features impacted audience perceptions
on these two factors (RQ3b). Interestingly, the valence dimension highly correlated
with perceived slant (r=.93, p<.001). Most features predicting perceived slant
Figure 3 Distribution of visual features across media outlets and candidates. Note:BB=
Breitbart; DK =Daily Kos; HP =The Huffington Post; NYT =The New York Times;UT=
USA Today; WP =The Washington Post;WSJ=The Wall Street Journal. Scales: happiness/
anger, 0–100; skin health, −100–100; face size, 0–100%.
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also influenced judgment on the valance dimension, including face size (β=−.16),
happiness (β=.43), anger (β=−.14), mouth openness (β=.10), skin health
(β=.08), and other faces’happiness (β=.07, all ps<.001). The number of other
faces also slightly increased valence (β=.04, p=.03; Figure 4a).
In contrast, the effects of visual attributes on dominance, which correlated with
perceived slant only to some degree (r=.23, p<.001), showed a different pattern.
A few attributes influenced dominance in the same direction of predicting valence,
such as face size (β=−.14, p<.001) and mouth openness (β=.09, p=.01). Yet
anger (β=.25) increased dominance, while happiness (β=−.13, both ps<.001)
acted negatively: these measures were in the opposite directions of predicting
valence. Completely happy and angry faces would impact dominance by −.1 and
Figure 4 Effects of visual features on perceived slant, valence, and dominance. Note:
Regarding other faces’happiness and eye openness, mean substitution was applied to photos
with only the candidate’faces. Candidate: 1 =Trump, 0 =Clinton. The three adjusted R
2
values are in the order of predicting perceived slant, valence, and dominance.
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+.8 on a 5-point scale, respectively. Skin redness (β=−.09, p=.01) and eye gaze
(β=.08, p=.02) also had small effects on dominance (Figure 4a).
Discussion
In summary, this research advances our understanding of visual bias in the follow-
ing ways. First, by integrating the objective checklist and subjective rating
approaches developed in prior research, this study extends conventional analysis,
which often focuses on the magnitude and direction of bias, to understanding how
partisan media bias is constructed in visual portrayals of politicians and how these
portrayals then influence audience interpretations. Regarding audience perceptions,
this research also extends visual favorability from a single positive–negative spec-
trum to a two-factor space that includes valence and dominance. Different visual
cues can exert both similar and reversed impacts on perceptions of these two
dimensions. And last, this research also demonstrates that the use of computer
vision tools greatly expands the scope of visual analysis and could better equip com-
munication scholars to study visual content, which is becoming increasingly ubiqui-
tous in our media environment.
Partisan bias in visual content
With a few exceptions, visual features that differentiate liberal from conservative
media largely overlap features that impact viewers’perceptions of slant, including
facial expressions, face size, and skin condition. Facial expressions, and particularly
happiness, play essential roles in shaping participants’perceptions of media slant
and impressions of politicians. Indeed, in face perception literature, perceived hap-
piness in faces and valance-related traits such as trustworthiness, intelligence, and
attractiveness often load on the same dimension in factor analysis (Oosterhof &
Todorov, 2008;Sutherland et al., 2013). In interviews, Lobinger and Brantner
(2015) also found that viewers heavily relied on politicians’facial expressions to
judge the slant in news images. Therefore, computationally-detected happiness in
politicians’faces could be a simplified but efficient proxy of visual slant. This mea-
sure might be particularly useful for information platforms that primarily circulate
visual data, such as Instagram and YouTube.
One genre of negative portrayals in media coverage might require further atten-
tion: images featuring candidates with a large face, occupying almost the entire
image, which highlights their skin flaws and negative emotional expressions.
5
Prior
research in face-ism, which often compares full-body with half-body images, has
shown a positive influence of facial prominence on impression formation (Archer
et al., 1983). The negative impact of face size found in this study somewhat contra-
dicts face-ism, but echoes what Mutz (2007) has termed as “in-your-face politics”in
television discourse and Grabe and Bucy’s (2009) interpretation of extreme close-
ups. Close-up shots of politicians make them seem to be in the face of viewers and
create a sense of discomfort and uneasiness among viewers, which could intensify
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viewers’preexisting negative feelings towards them. Furthermore, in fashion and
advertising, media professionals can manipulate skin condition (e.g., remove wrin-
kles) to make models look more attractive in photos. In political coverage, media
outlets might also intentionally make a politician look unfavorable by highlighting
facial skin flaws. We should also note that both candidates examined here are
White. The effects of skin condition might be more complicated if politicians of
other races are considered, which could be an arena for future research.
Audience perceptions of favorability
Echoing prior research, this study revealed the multiple dimensions people use to
evaluate politicians and highlights the need to study the effects of visual portrayals
on different trait perceptions (Grabe & Bucy, 2009). Confirming previous research
in face perception, a two-factor structure emerged from the data (Oosterhof &
Todorov, 2008). While perceived slant converges with the valence dimension, domi-
nance forms a separate dimension that should require further attention. These two
factors do not completely converge. For example, some pictures intended to make
candidates look bad by emphasizing their angry expressions and aggressive beha-
viors, like yelling and shouting, might simultaneously render them as more power-
ful and dominant.
Prior research has frequently shown the effects of trait perceptions on voting
preferences (Caprara & Zimbardo, 2004), but where do people get their impressions
of politicians? This study suggests that visual portrayals of politicians might be one
source of trait perceptions. In the results, across different media outlets, Trump
images expressed more anger and less happiness than Clinton, which should make
him look less favorable and friendly but more dominant and aggressive. Indeed, a
study conducted before the election showed that people considered Clinton as more
caring and competent and Trump as more dominant (Kakkar & Sivanathan, 2017).
Furthermore, trait perceptions on different dimensions might have distinct impacts
on candidate preferences. Voters might prefer more dominant, aggressive leaders to
more competent or caring ones when feeling threatened by outsiders or in uncertain
situations (Kakkar & Sivanathan, 2017). Future work could experimentally examine
how dominance in visual portrayals influences viewers’judgment of candidates and
subsequent voting behaviors.
With the increasing visibility of female politicians in contemporary politics, it is
also important to further bridge partisan bias and gender bias research. Gender bias
might operate on multiple levels. First, media content might associate different
norms with female and male politicians. In the results, Trump was portrayed using
images with larger faces, less happiness, more anger, and fewer people around than
Clinton. This could result from an unfavorable treatment of Trump across media
outlets, as well as gender bias in face-ism and gender stereotypes that expect women
to be more friendly and sociable and less aggressive (Archer et al., 1983;Prentice &
Carranza, 2002). Moreover, the effects of visual cues on audience perceptions might
differ between female and male politicians. Additional ordinary least squares
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regressions using photos of Clinton and Trump partially affirmed this possibility
(Figure 4b and c). For example, the positive impact of happiness on perceived slant
was more salient regarding Clinton’s images than Trump’s, potentially reflecting the
gender norm in facial expressions that it was more proper or rewarding for women
than men to smile (Plant et al., 2000). The negative effect of face size on dominance
was also more pronounced for Clinton than Trump. Nevertheless, neither Clinton
nor Trump represents all female or male politicians. Future research could include
multiple female and male politicians to better study the interplay between political
and gender biases.
The diversity in audience interpretations of visual content might also require
our attention (Lobinger & Brantner, 2016). As Figure 4b and c shows, photos of
candidates that matched (mismatched) viewers’political orientation were rated as
more positive (negative), independently from visual attributes. This pattern was
especially salient for Trump, a controversial figure who invited polarized responses
from participants. As one crowdsourced worker commented at the end of the rating
survey: “It was very difficult to rank Trump anything but the lowest in honesty no
matter what the picture was.”We also see that, compared with slant and valence,
viewers’agreement on dominance was relatively low. This shows that crowdsourced
workers can reach an agreement regarding certain evaluations, but there might be
some inherent variability in people’s interpretations of visual dominance, which
requires further study. Future research could also study how other individual char-
acteristics, such as political knowledge and visual literacy, might affect how viewers
attend to and process visual portrayals of politicians.
Computer vision, limitations, and future research
This research demonstrates the potential of applying computer vision tools in ana-
lyzing large-scale visual media. However, several limitations exist. Current face
detection services only work well with near-frontal faces. If algorithms fail to detect
faces in a photo, this might already indicate an unfavorable portrayal of politicians
(e.g., blocking a politician’s face); therefore, this study’sfindings should only apply
to images featuring identifiable faces of politicians. In addition, the computer vision
services examined could not accurately identify facial expressions such as fear and
sadness. This is unfortunate, as different negative emotions often produce differing
effects regarding the evaluation of valence and dominance (Knutson, 1996).
Nevertheless, this low accuracy could partially be due to the consideration that
some expressions were not prevalent in the dataset and some were highly ambigu-
ous: raters themselves couldn’t agree upon which face should count as fearful
(IRR =.50). Also, visual features such as politicians’gestures and activities might
also reflect media bias, but could not be captured by the currently-available com-
puter vision services. As the field of computer vision grows, future research could
also incorporate these attributes.
One limitation of this study is that the images were resized and decontextualized
from their original content. News stories associated with images may further
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influence how photos of politicians are picked up by media practitioners and inter-
preted by audiences. The design components in news websites might also reflect
media bias. For example, the size of a photo might reflect the visibility a media out-
let intends to give to a politician and influence readers’allocation of attention (Barrett
& Barrington, 2005). Bringing tools of already widely-applied computational textual
analysis, the next step in this line of research might be multi-modal analyses that
investigate the interplay between textual and visual biases in media outlets. This
study also only looked at still images, which are only one component of visual polit-
ical communication. Future work might also apply computer vision techniques to
analyze moving images, such as online videos.
Acknowledgments
This work was supported by the Dissertation Research Fellowship at the Annenberg
School for Communication. The author thanks Sandra González-Bailón, Paul
Messaris, Jessa Lingel, Michael X. Delli Carpini, Sharrona Pearl, members of the
DiMeNet research group, and four anonymous reviewers for their feedback on ear-
lier versions of this article. The author also thanks Hyun Suk Kim for his help in
data analysis.
Notes
1 Although BBC is a British outlet, it attracts a sizable U.S. readership and has been
classified as left-leaning by prior research (Mitchell et al., 2014), so it was included in our
sample. Two strategies were used to address the concern about the potential bias in search
engine results: (a) this study predefined a list of media sources and limited each search to
one source, so whether Google prioritized certain sources should have had a limited
impact on the results; and (b) all the images returned by Google were retrieved, so
whether Google ranked certain types of entries ahead of others should not have generated
substantial biases in the data.
2 The face set included the two candidates and their family members (e.g., Ivanka Trump),
running mates (e.g., Tim Kaine), competitors (e.g., Bernie Sanders), third-party
candidates (e.g., Gary Johnson), and other figures who frequently appeared in the
campaign (e.g., Barack Obama). For each person, the face set included about 10 images
that covered a diversity of facial expressions and luminance conditions. To validate results
from Face++, the study manually coded 400 images selected from all the retrieved images
on whether the image had visible faces of Clinton and Trump. The analysis tried a series
of thresholds (.60, .65, .70, …, .95) as cut-offpoints to accept whether the face should be
determined as Clinton or Trump. The overall accuracy rate was maximized when the
threshold of .75 was used both for identifying Clinton (accuracy =97.75%, false negative =
2%) and Trump (accuracy =97.25%, false negative =1.5%).
3 Participants reported their gender (female =50.0%), age (M=39.3, SD =12.6), race/
ethnicity (multiple answers allowed: Hispanic =7.6%; African American =8.6%; Asian =
8.1%; White =83.2%), education (high school or less =7.9%; some college =21.0%;
Associate’s degree =13.7%; Bachelor’s degree =37.4%; Master’s degree or higher =
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19.8%), political ideology (M=3.73, SD =1.78; 1 =extremely liberal,7=extremely
conservative), and party affiliation (M=3.86, SD =2.13; 1 =strong Democrat,7=strong
Republican). The last two (r=.82) were combined into one political orientation scale. A
screening survey sampled a roughly equal number of women and men and of Democrats
(34.3%) and Republicans (33.8%).
4 To assess the accuracy in other computer vision features, on three-point scales (2 =in the
middle/can’t decide), this study also manually coded a subset of 150 images on pitch (1 =
head bowing, 3 =raising; r=.60), roll (1 =head tilting right, 3 =left; r=.72), and yaw
(1 =facing right, 3 =left; r=.78) angles of the face; eye (1 =closed, 3 =open; r=.71)
and mouth openness (1 =closed, 3 =open; r=.67); eye gaze direction (1 =looking
elsewhere; 3 =nearly vertical to the image; r=.50); and skin health (1 =visible skin
flaws, 3 =smooth and healthy; r=.48), which well correlated with computationally
calculated features.
5 Face size negatively correlated with skin health (r=−.21) and happiness (r=−.09),
implying that media combined these features to portray a candidate positively/negatively.
Using regressions, tests of interactions suggested that the impact of happiness (but not
skin health) on slant (β=−.04, p=.048) and valence (β=−.05, p=.012) was larger for
images with smaller faces. Given the potential mixed effect of face size, the quadratic term
of face size was also tested and was significant in predicting slant (β=.09, p=.007) and
valence (β=.09, p=.009), implying that images with extremely large faces did not
further reduce favorability compared with only fairly large ones.
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