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Abstract

In this paper, we utilize a module from the Cooperative Congressional Election Study to explore how individual perceptions of media bias changed over the course of the 2016 presidential campaign. While previous literature has documented the role of partisan affiliation in perceptions of bias, we know considerably less about how these perceptions change during a presidential election. Consistent with existing theories of attitude change, perceptions of bias polarize with strong Democrats moving toward believing the media were biased against Hillary Clinton (and in favor of Donald Trump) and independent-leaning Republicans moving toward believing the media were biased against Donald Trump. At the end of the 2016 election, more individuals believed the media were biased against their side. These effects were moderated by how much attention individuals paid to the campaign.

Keywords

Media bias hostile media effect public opinion political communication 2016 presidential election media and politics

How did perceptions of media bias change over the course of the 2016 presidential campaign? While considerable research has investigated the causes and consequences of media bias, few studies have investigated changes in individual-level perceptions of media bias over the course of a presidential election (Huge and Glynn, 2010). The 2016 campaign presents an ideal context for such a study. First, Donald Trump routinely claimed media bias as part of a rigged political system undermining his campaign (Goidel et al., 2019). From the existing literature, we know that elite cues—more than actual media content—drive perceptions of media bias (Domke et al., 1999; Smith, 2010; Watts et al., 1999). More generally, partisan cues serve as an important predictor of individual attitude change with opinion shifting in the direction of cues provided by trusted elites (Boudreau and MacKenzie, 2014; Bullock, 2011, 2019; Dalton et al., 1998; Downs, 1957; Ladd, 2010; Peterson, 2017).¹

Second, the 2016 campaign provided a surprising victory for Donald Trump after most experts and forecasters predicted a Hillary Clinton victory. Indeed, some estimates verged on near certainty, gauging Clinton’s probability of winning the election at 90% or greater (Jackson, 2016; Katz, 2016).² Based on previous literature, we have good reason to expect a surprise loss should increase perceptions of bias, particularly for the losing (Democratic) party. Unique events, including elections, economic downturns, and extreme weather events, can shift individual attitudes across a range of issues as individuals update beliefs based on real-world events (Albarracin and Shavitt, 2018; McCann, 1997; Margalit, 2019; Page and Shapiro, 1992). For example, the election of Barack Obama positively affected racial attitudes by reducing stereotypical thinking about African-Americans (Goldman and Mutz, 2014; Plant et al., 2009; Welch and Sigelman, 2011) while simultaneously increasing racial resentment and racializing signature policy issues such as health care reform (Tesler and Sears, 2010). Who wins or loses an election can also alter partisan views of the economy and even shape subsequent economic behaviors (Bartels, 2002; Gerber and Huber, 2009, 2010). On a related note, research demonstrates that election losses are often rationalized in terms of motivated reasoning, viewing the election as unfair (Hollander, 2014), decreasing satisfaction with democratic processes (Anderson and LoTempio, 2002; Blais and Gélineau, 2007; Daniller and Mutz, 2019; Lelkes, 2016), increasing conspiratorial thinking (Miller et al., 2016), and increasing distrust and dislike of opposition partisans (Levendusky, 2013). These “legitimacy gaps” are magnified in media systems characterized by a partisan press, though the effects are also contingent on media exposure, with greater exposure increasing the gap (Lelkes, 2016).

While these studies do not directly connect to perceptions of media bias, we have good reason to believe these attitudes would be affected, as media bias is routinely singled out as the primary reason for unfavorable election outcomes and was explicitly blamed for Hillary Clinton’s loss in 2016 (Clinton, 2017; Ladd and Podkul, 2019; Watts and Rothschild, 2017). More generally, attitudes toward the media are shaped by the hostile media phenomenon, meaning that partisans perceive news coverage as hostile to their point of view even when news coverage is neutral (D’Alessio, 2003; Feldman, 2011; Hansen and Kim, 2011; Perloff, 2015; Vallone et al., 1985). The more involved or engaged partisans are with news content and the more intense their partisan preferences, the greater the perceived bias (Hansen and Kim, 2011; Lee, 2005; Oh et al., 2011). Finally, individuals have a tendency to “shoot the messenger” when confronted with, and trying to make sense of, unexpected outcomes. This occurs even when the deliverer of the bad news objectively had no control over the outcome (John et al., 2019).

Modeling changes in perceptions media bias

The pervasiveness of the news media in contemporary politics means that few individuals enter an election with a blank slate of attitudes toward the media. Not only do they have their own everyday experiences they can draw upon, they also have access to political messaging about how the media are performing and about which side they favor or disfavor (Arceneaux et al., 2012; Ladd, 2012; Smith, 2010). Individuals then begin an election with a baseline set of beliefs about media bias (Arceneaux et al., 2012). Those beliefs are informed by their partisan predispositions, how much attention they pay to news about politics, partisan messages (or cues) signaling evaluations of media performance, and the electoral process and outcome. Attitudes toward the media change as new information (or considerations) are brought to bear on the attitude in question (Eagly and Chaiken, 1993; Peterson and Kagalwala, forthcoming; Zaller, 1992). In the case of the 2016 presidential election, these new considerations came in the form of a surprising election outcome and the constant drum-beat of media bias claims emanating from the Trump campaign. The Trump claims were hardly new. Republicans have long complained that the mainstream news media treat conservative candidates and causes unfairly (Ladd and Podkul, 2019; Watts et al., 1999). What was new was the connection made by Trump to a rigged political and economic system and questions of democratic legitimacy (Goidel et al., 2019).

Empirically, the contextual factors in the model, the broader information environment, and the surprising election result are set by our focus on the 2016 election and cannot be measured within a model situated within a single election year. This simplifies the empirical model such that changes in perceptions of media bias are function of individual-level partisanship and political interest. This can be expressed as follows:

(1) Media bias_t2 = Media bias_t1 + Party affiliation + Political interest + (Party affiliation x Political interest)

(2) Bias change_(t2–t1) = Partisan affiliation + Political interest + (Party affiliation x Political interest)

We expect that both Republicans and Democrats would perceive the media as biased against their side and that these perceptions should increase after the election, though for very different reasons. Republican perceptions should be guided by elite cues, particularly ongoing claims of media bias. Because these claims are not new and Republicans perceived greater media bias prior to the election, less partisan and less interested Republicans should be more likely to evidence significant attitude change. Democrats, in contrast, should be affected by the surprising election loss and subsequent partisan rationalizations blaming the election loss on the media. In contrast to Republicans, strong partisan and highly interested Democrats should shift toward perceiving greater media bias. First, their baseline perceptions of bias were set lower prior to the election, allowing for greater upward movement, particularly among strong partisans. Second, the unexpected outcome left Democrats searching to make sense of the election (John et al., 2019). For many Democrats, blame fell on the media and its coverage of the Clinton email scandals during the final week of the campaign (Clinton, 2017; Watts and Rothschild, 2017). Collectively, the 2016 election should have polarized opinion on media bias with both Democrats and Republicans perceiving greater bias at the end of the election.

Data and methods

To address how perceptions of bias changed over the course of the election, we utilize a module of the 2016 Congressional Cooperative Election Survey (CCES) fielded prior to and immediately following the election (Ansolabehere and Schaffner, 2017). The pre-election sample included 1000 respondents interviewed from 4 October 2016 to 6 November 2016; 815 respondents were re-interviewed from 9 November 2016 to 12 December 2016. The data analyzed below only include those respondents interviewed during both waves.

Perceptions of bias were operationalized using the following question: “Thinking now about news coverage of the candidates over the course of the campaign, would you say that, on balance, news coverage has been biased in favor of Hillary Clinton, fair and balanced, or biased in favor of Donald Trump?” Respondents were asked to place their responses on an 11-point scale ranging from 0 (biased in favor of Hillary Clinton) to 10 (biased in favor of Donald Trump). Changes in perceptions of media bias were measured as the difference between post-election and pre-election responses (t2-t1). The mean response from the pre-election wave was 3.60 (SE = 0.14) indicating that the average respondent perceived that Hillary Clinton received more favorable news coverage. This shifted only slightly after the election ( $\bar{x}$ = 3.74, SE = 0.15). Though the differences between pre- and post-election perceptions of bias were stable in the aggregate, approximately 60% of respondents shifted over the course of the campaign.

Among partisans the story is more nuanced (Panels B and C, Figure 1). Republicans reliably graded the media as pro-Clinton across waves of the survey but perceived even greater bias after the election ( $\bar{x}$ = -0.19; SE = 0.12). Democrats, in contrast, perceived less bias prior to the election but shifted significantly shifted toward the pro-Trump pole following the campaign ( $\bar{x}$ = 0.41, SE = 0.12). Even with this shift, Democrats perceived the media as less biased than Republicans. Unlike the difference in grand means across waves, these partisan differences were statistically significant

( $\bar{x}$ = 0.60; SE = 0.20).

Figure 1.

Perceptions of media bias among partisans.

Independent variables: our primary theoretical interest is in the effect of partisan affiliation on changes in perceptions of bias.³ Because we expect nonlinear effects across partisan affiliation and partisan strength, we include separate dichotomous indicators for partisan affiliation capturing both partisan identity and partisan strength. Independents serve as the baseline and are not included in the model. The effects of partisan affiliation should also be moderated by political interest.⁴ Political interest gauges how closely respondents paid attention to government and public affairs ranging from 0 indicating respondents who hardly paid any attention at all to 3 indicating respondents who pay attention most of the time (M = 1.76; SE = .04). A majority of respondents (56.4%) said they paid attention most of the time, 27.6% said they paid attention some of the time, 10.4% only now and then, and 5.7% hardly at all.⁵

Results

Before we present the models estimating change in bias perceptions, we begin by examining the predictors of perceived media bias before and after the election. The results of these regression models are presented in Table 1 and are visually displayed in Figure 2. Because of potential floor effects at the lower end of the scale (reflecting Republican perceptions of liberal media bias), we use Tobit, rather than ordinary least squares (OLS), to estimates our models. For ease of presentation, we present the results separately for the most attentive respondents (those who follow politics most of the time) and less attentive respondents (those who follow politics some of the time, only now and then, or hardly at all).⁶ Tests of the interaction effects are presented in the Online Appendix. Consistent with the hostile media phenomenon, we expect more attentive respondents would perceive greater bias against their side and that these perceptions would increase over the course of the campaign.

Table 1.

Tobit models predicting perceptions in pre-election and post-election media bias.

	All respondents (N = 801)		High political interest (N = 431)		Low political interest (N = 370)
	Media bias (pre-election)	Media bias(post-election)	Media bias (pre-election)	Media bias (post-election)	Media bias (pre-election)	Media bias (post-election)
Strong Democrat	2.99	4.38	4.71	6.11	0.89	1.74
	(0.54)**	(0.65)**	(0.90)**	(1.05)**	(0.50)	(0.68)*
Weak Democrat	0.90	1.65	3.94	4.33	–0.87	0.06
	(0.68)	(0.60)**	(1.14)**	(1.19)**	(0.74)	(0.57)
Leans Democrat	2.02	2.18	4.35	4.41	–0.02	0.14
	(0.53)**	(0.69)**	(0.91)**	(1.01)**	(0.53)	(0.83)
Leans Republican	–3.47	–4.51	–3.49	–4.17	–3.17	–4.69
	(0.79)**	(0.79)**	(1.38)*	(1.37)**	(0.75)**	(0.86)**
Weak Republican	–2.55	–2.38	–2.20	–3.07	–2.85	–2.03
	(0.68)**	(0.73)**	(1.19)^	(1.26)*	(0.81)**	(0.92)*
Strong Republican	–2.40	–2.86	–1.75	–2.94	–2.07	–1.96
	(0.68)**	(0.82)**	(1.02)^	(1.24)*	(1.12)^	(1.20)
Political interest	–0.65	–0.65
	(0.20)**	(0.25)**
Constant	4.45	4.18	1.17	1.22	4.44	4.01
	(0.51)**	(0.51)**	(0.78)	(0.86)	(0.36)**	(0.31)**

Note: Cell entries are coefficients from Tobit models. (Standard errors are in parentheses).

p < .10; *p < 0.05; **p < 0.01.

Figure 2.

The effect of partisan identification on perceptions of media bias before and after the 2016 presidential election.

The results in Table 1 and Figure 2 largely reflect these expectations. First, partisanship and partisan strength are associated with perceptions that the media were biased against their side and in favor of the opposition. Notably, we observe this effect within the context of public opinion more generally oriented toward believing the media are biased against Trump (and in favor of Clinton). Second, there are also expected differences across partisan strength. Strong Democrats were more likely than weak Democrats or independent leaning Democrats to shift toward perceiving greater media bias against Hillary Clinton after the election. For Republicans, partisan strength matters less. Independent-leaning and weak Republicans were as likely to perceive partisan bias as strong Republicans. Third, political interest magnifies perceived biases for Democrats, meaning more attentive Democrats were more likely to perceive the media as biased against Hillary Clinton. For Republicans, the effects work in the opposite direction. Less attentive Republicans and independent-leaning Republicans were more likely to shift toward perceiving greater media bias following the election. Fourth, a quick glance at the coefficients across models provides suggestive evidence that partisanship becomes more important to perceptions of bias after the election and that this effect is particularly pronounced for strong Democrats and independent-leaning Republicans.

Were we to end the analysis here, the findings would largely replicate the well-known and well-documented hostile media phenomenon whereby partisans perceive the media as biased against preferred candidates and causes. This leaves open the question as to how these perceptions shift as the result of a surprising election outcome. Table 2 and Figure 3 presents the findings for change in bias perceptions. We model this in two ways consistent with the models presented above: (a) predicting media bias at t2 using prior media bias (t1) as a predictor; and (b) as the change in bias perceptions across the waves of the survey. In the models with estimating media bias, we estimate the models using Tobit. In our models of bias change, we estimate the models using OLS. In two-wave panel data, the OLS results presented here are equivalent to a fixed-effects model.

Table 2.

Models of change in media bias on partisan affiliation and political interest.

	All respondents (N = 801)		High interest (N = 431) respondents		Low interest (N = 370) respondents
	Media bias (post-election)	Bias change(t2–t1)	Media bias (post-election)	Bias change(t2–t1)	Media bias(post-election)	Bias change(t2–t1)
Media bias t1	0.80		0.96		0.49
	(0.08)**		(0.12)**		(0.10)**
Strong Democrat	2.59	1.01	2.74	0.94	1.31	0.71
	(0.56)**	(0.34)**	(0.86)**	(0.39)*	(0.65)*	(0.60)
Weak Democrat	1.17	0.51	1.60	0.14	0.39	0.75
	(0.57)*	(0.52)	(0.97)^	(0.71)	(0.59)	(0.70)
Leans Democrat	0.98	0.05	1.22	–0.18	0.16	0.22
	(0.57)^	(0.42)	(0.81)	(0.49)	(0.78)	(0.67)
Leans Republican	–2.63	–0.40	–2.40	–0.38	–3.43	–0.67
	(0.71)**	(0.44)	(1.19)*	(0.60)	(0.83)**	(0.55)
Weak Republican	–0.87	0.22	–1.64^	–0.43	–0.95	0.75
	(0.59)	(0.38)	(0.93)	(0.44)	(0.79)	(0.59)
Strong Republican	–1.46	0.04	–1.67	–0.19	–1.14	0.16
	(0.69)*	(0.45)	(0.75)*	(0.33)	(1.20)	(1.12)
Political interest	–0.35	0.03
	(0.24)	(0.19)
Constant	0.70	–0.22	–0.94	0.11	1.83	–0.35
	(0.65)	(0.48)	(0.63)	(0.28)	(0.60)**	(0.36)

Note: Lagged models are estimated using Tobit. Change models are estimates using fixed effects (ordinary least squares (OLS)). (Standard errors are in parentheses).

p < .10; *p < 0.05; **p < 0.01.

Figure 3.

The effect of partisan affiliation on change in perception of media bias by political interest.

These findings reveal that most notable shift in perceptions of media bias occurred among strong Democrats. First, Democrats moved toward perceiving greater bias against Hillary Clinton after the election, with the effects most pronounced among those strong Democrats who paid close attention to the election. While we have limited evidence on this point, these individuals most likely re-evaluated and updated their baseline attitudes toward the media in light of the surprising election outcome (Arceneaux et al., 2012). Second, Republicans also shifted toward perceiving greater media bias, though the effects were most pronounced among independent-leaning Republicans who paid less attention to the campaign. Strong and weak Republican partisan identifiers already believed the media were biased against Donald, Trump, so we see less movement among these groups. More broadly, perceptions of media bias polarized over the course of the campaign with both Republicans and Democrats moving away from believing the media were fair and balanced and toward believing the media were biased against their side.

Conclusions

What is the effect of the highly contested 2016 election on perceptions of media bias? First, consistent with the previous literature we find that perceptions of media bias reflect the hostile media phenomenon (Gunther, 1992; Gunther et al., 2001, 2012; Vallone et al., 1985). Strong partisans (Democrats and Republicans) are more likely to perceive the media as biased against their side. This does not occur in equal measure as Republicans, reflecting elite cues and Fox News niche marketing, were already more inclined to believe that the media were biased in favor of Hillary Clinton. This is not simply a partisan effect as independents were also more inclined to believe the media were biased in favor of Clinton, though less so than Republicans. This reflects the dominant narrative of media bias perpetuated by Republican elites, though not necessarily reflected in studies of media content of the 2016 campaign (Patterson, 2016).

More importantly, the tendency to view the media through a partisan lens increased over the course of the 2016 campaign. Strong Democrats and independent-leaning Republicans were most significantly affected. We contend this occurred for different reasons. Partisan Democrats were responding to the surprising election outcome while independent-leaning Republicans were responding to Donald Trump’s claims of bias throughout the campaign. Regardless, fewer respondents perceived the media as “fair and balanced” after the election.

One limitation of the current study is that, while neatly bracketed around the 2016 election, we do not have additional data points allowing us to track individual attitude change throughout the Trump Administration. Were these lasting effects or a short-term change that recovered after Trump assumed the presidency and the mainstream media returned to a more adversarial role? Available evidence indicates the shift among Democrats was a short-term decline as opposed to a more lasting effect. The New York Times and Washington Post digital subscriptions have tripled since 2016 (Fischer, 2020); and, though trust in the news media dipped to its nadir following the 2016 election, it has increased significantly among Democrats and Independents since that election. (Brenan, 2020). This would seem to confirm recent research findings that attitudes towards the media, including perceptions of media bias, are sensitive to individual partisanship, partisan cues, real-world events, and media content (Arceneaux et al., 2012; Peterson and Kagalwala, 2019).

Supplemental Material

sj-pdf-1-rap-10.1177_2053168020987441 – Supplemental material for Changes in perceptions of media bias

Supplemental material, sj-pdf-1-rap-10.1177_2053168020987441 for Changes in perceptions of media bias by Kirby Goidel, Nicholas T. Davis and Spencer Goidel in Research & Politics

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Kirby Goidel

Supplemental materials

The supplemental files are available at http://journals.sagepub.com/doi/suppl/10.1177/2053168020987441. The replication files are available at .

Notes

Carnegie Corporation of New York Grant

This publication was made possible (in part) by a grant from the Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author.

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