Sage Journals: Discover world-class research

Abstract

Conservatives are often blamed for spreading misinformation, but it is unclear whether certain situations trigger them and, if so, why. The authors examine situations that are politically polarized, meaning the topic and/or its framing conveys conflict, discord, or disagreement between the two main political parties (conservatives and liberals). The authors study whether conservatives react to polarized situations by spreading ingroup-skewed political misinformation that is objectively inaccurate but not necessarily understood to be false and whether liberals are less reactive. Using a multimethod approach, the authors conduct six studies, including analyses of statements by public figures and speeches by U.S. presidents, as well as controlled experiments. The results indicate that in polarized situations, conservatives’ need for ingroup dominance is elevated, so they convey more misinformation than liberals. In less polarized situations, conservatives’ need for ingroup dominance is tempered, reducing their misinformation conveyance. These findings suggest that misinformation should not be blamed solely on the individual trait of conservativism, as polarized situations exaggerate conservative motives and behaviors. While news media, social media, political figures, and others may be incentivized to emphasize political polarization to gain audiences and bolster engagement, the resulting misinformation harms truth, trust, and democracy. Possible remedies include improved fact-checking and media literacy education.

Keywords

misinformation political ideology polarization situational effects news media social media

Misinformation is broadly defined as “information that is incorrect, possibly by accident” (Scheufele and Krause 2019, p. 7662). Although misinformation is objectively incorrect, the individual sharing it may not deliberately seek to spread falsehoods (i.e., it may not be intentional disinformation; Scheufele and Krause 2019). Misinformation has been identified by the World Economic Forum (2024) as the world's most significant risk at present. A recent survey conducted in 142 countries found that 60% of internet users perceive online misinformation as posing a high risk to them personally (Knuutila, Neudert, and Howard 2022). Twenty percent of the visual political content on social media has been deemed misinformation (Yang, Davis, and Hindman 2023). The present research focuses on a particularly damaging and widespread type of misinformation: ingroup-skewed political misinformation that bolsters one's political party and/or disparages the rival party (Vosoughi, Roy, and Aral 2018).

Misinformation has distorted public discourse on a variety of issues, including public health (Kreps and Kriner 2022), environmental protection (Chinn, Hart, and Soroka 2020), and election integrity (Haber et al. 2021). With approximately 3 billion people worldwide poised to vote in upcoming elections (World Economic Forum 2024), political misinformation poses an immediate threat to the legitimacy of these democratic processes and the government officials they elect. Misinformation pertaining to the 2020 U.S. presidential election, which incited a violent attack on the U.S. Capitol, exemplifies just one of the harms (McCarthy 2021). Despite fact-checking initiatives by major social media platforms (e.g., Meta), news media (e.g., Reuters, AP News), and nonprofits (e.g., PolitiFact, FactCheck.org), misinformation is pervasive and continues to grow (McDonald 2021). Thus, we need a better understanding of who is most likely to spread misinformation, in which situations, and why.

Conservatives tend to be more likely to spread political misinformation than liberals, as initially theorized by Jost et al. (2018) and supported by numerous empirical studies (Grinberg et al. 2019; Guess, Nagler, and Tucker 2019; Osmundsen et al. 2021). For instance, Allcott and Gentzkow (2017) found that during the 2016 U.S. election cycle, out of 38 million politically misinformative posts on Facebook, 30 million (79%) were skewed conservative, whereas 7.8 million (21%) were skewed liberal. Yang, Davis, and Hindman (2023) found that posts with imagery from conservatives were five to eight times more likely to be misleading than posts from liberals.

However, it is not yet known whether conservatives are more prone to spread misinformation across virtually all situations, or whether certain situations may trigger them. In this research, we focus on politically polarized situations where the topic and/or its framing conveys conflict, discord, or disagreement between the two main political parties: conservatives and liberals (Bavel et al. 2021; Han and Federico 2017). News media, social media, political and public figures, and others may intentionally or inadvertently polarize a situation (Wilson, Parker, and Feinberg 2020). For example, when reporting on the expulsion of a U.S. congressperson, the U.S. Wall Street Journal described it as a “tense vote” (Ferek and Vielkind 2023), whereas the U.K. newspaper The Guardian framed the same event more neutrally as a “bipartisan vote” (Reed 2023). Both characterizations are accurate but differ in polarization. We ask whether polarization can increase misinformation spread by conservatives and, if so, why.

Based on theories of political ideology, we posit that conservatives will convey more ingroup-skewed political misinformation than liberals because they value ingroup dominance more strongly; however, polarization will trigger this value difference and behavior. Researchers have found that conservatives are more likely than liberals to seek ingroup dominance, that is, they want their ingroup to dominate and be superior to other groups (Graham, Haidt, and Nosek 2009; Janoff-Bulman 2009; Jost, Federico, and Napier 2009; Ordabayeva and Fernandes 2018). Conveying ingroup-skewed misinformation helps with ingroup dominance, which should appeal to conservatives as it is a core value, but that value may not be salient. Among conservatives, the need for ingroup dominance may become salient in politically polarized situations.

Our findings indicate that conservatives in polarized situations have an elevated desire for ingroup dominance, which provokes them to spread ingroup-skewed misinformation, whereas liberals do not react this way. In addition, conservatives and liberals do not differ in less polarized situations. Our research appears to be the first to show that conservatives’ tendency to convey misinformation is driven in part by their need for ingroup dominance, provoked by polarization. More broadly, we show that misinformation is caused not only by a person's ideology but also by situational factors. The marketplace often incentivizes polarization to increase audiences and engagement (Berry and Sobieraj 2013; Wilson, Parker, and Feinberg 2020), inadvertently provoking misinformation. There are viable solutions, though, including better fact-checking to remove the misinformation and media literacy education to teach misinformation detection.

Theoretical Framework and Hypotheses

Political Misinformation and Ideology

Both conservatives and liberals contribute to the dissemination of political misinformation (Hochschild and Einstein 2015), yet research has shown a tendency for conservatives to disseminate it more than liberals (Allcott and Gentzkow 2017; Grinberg et al. 2019; Guess, Nagler, and Tucker 2019; Hameleers and Minihold 2022; Nikolov, Flammini, and Menczer 2021; Osmundsen et al. 2021). It has been argued that conservatives are more prone to spread misinformation due to their greater exposure to it (Grinberg et al. 2019; Guess, Nagler, and Tucker 2019; Guess et al. 2021), social network homogeneity (Allcott and Gentzkow 2017; Nikolov, Flammini, and Menczer 2021), cognitive vulnerability (Pennycook and Rand 2019), desire for chaos (Lawson and Kakkar 2022), desire for alternative reality (Hameleers and Minihold 2022), major news doubt (Lutzke et al. 2019), or major news nonrepresentation (Osmundsen et al. 2021). However, one study relating conservatism to misinformation has found mixed results (Pennycook and Rand 2019), and other studies have found null results (Ahmed and Gil-Lopez 2022; Hopp, Ferrucci, and Vargo 2020; Horner et al. 2021; McPhetres, Rand, and Pennycook 2021; Pereira, Harris, and Van Bavel 2023).

The divergent results have prompted a few researchers to consider potential moderators. Lawson and Kakkar (2022) find that conservatives share more misinformation than liberals, but only if they are low in conscientiousness, meaning they have a low propensity to “follow the rules of society, maintain social decorum, and think before acting” (p. 1155). There is no difference between highly conscientious liberals and conservatives. Overall, though, previous research has largely failed to study potential moderators that could impact how political ideology may affect misinformation spread. A table in Web Appendix A summarizes this research.

Situational Polarization as a Moderator

Due to the prevalence of political polarization, the current research focuses on it as a possible moderating factor. In the United States, political polarization is characterized by an ideological divide between conservatives (Republicans) and liberals (Democrats) in their stances on political and social issues, initiatives, and individuals (Fiorina and Abrams 2008). Surveys conducted by the Pew Research Center (Dunn 2020; Laloggia 2018) and the Public Religion Research Institute (2019) indicate that polarization is pervasive when political topics are discussed in the United States, including the role of government (Public Religion Research Institute 2019), government funding for different programs (Laloggia 2018), and the severity of specific national problems (Dunn 2020). Polarization intensifies during presidential and other elections, whipped up by politicians seeking to get out the vote, obtain donations, and win (Schaeffer 2020). Moreover, social media platforms, by tailoring their algorithms to maximize view engagement including likes and shares, often amplify polarization (Barnidge 2015; Bavel et al. 2021; Finkel et al. 2020).

Polarization has many adverse social outcomes; for instance, it can generate social misperceptions and undermine civility and norms of mutual respect in political discourse (Ahler 2014; Finkel et al. 2020). Research has also linked polarization to misinformation, but it has mainly focused on polarization stemming from individual characteristics such as extremist views, rather than situational factors (Bessi et al. 2016; Jenke 2023; Marino and Iannelli 2023; Osmundsen et al. 2021). Hence, we do not know whether situational polarization affects misinformation spread or whether conservatives and liberals respond differently to it.

Motive to Achieve Ingroup Dominance

A key value difference between ideological conservatives and liberals is their motive to attain ingroup dominance, meaning their desire for the ingroup to dominate and be superior to outgroups (Ordabayeva and Fernandes 2018; Pratto et al. 1994; Womick et al. 2019). Social dominance theory suggests that this difference may originate from rival viewpoints on hierarchical social structures, because conservatives typically view dominance-based hierarchies as legitimate, whereas liberals see them as illegitimate and advocate for equality (Jost et al. 2003; Ordabayeva and Fernandes 2018). Moreover, according to moral foundation theory (Graham, Haidt, and Nosek 2009), conservatives typically prioritize binding moral values that promote ingroup cohesion, such as loyalty, authority, and purity. In contrast, liberals tend to focus on individualizing moral values like care and justice, prioritizing the rights of individual members of society over their ingroup interests.

Another theory, the model of moral motives, suggests an ideological difference in how people form groups (Janoff-Bulman and Carnes 2013). Conservatives tend to form groups based on shared social identity, leading to impermeable intergroup boundaries that aim to protect and elevate their ingroup. Liberals, conversely, tend to form groups based on shared goals of social justice, resulting in permeable intergroup boundaries and an emphasis on intergroup equity. Despite differing perspectives, these theories collectively suggest that conservatives desire ingroup dominance, and liberals less so.

However, situational factors could affect people's felt motivation to attain ingroup dominance (Duckitt 2006; Jost et al. 2003). A situation that may trigger this motivation could be political polarization: that is, the extent to which the topic discussed and/or the way the topic is framed elicits conflict, discord, and disagreement between political parties (Bavel et al. 2021; Kim and Zhou 2020). Research finds that polarization heightens the salience of people's ideological motives (Kaikati et al. 2017). Thus, we posit that polarized situations, by accentuating intergroup divisiveness, may temporarily activate the ingroup dominance motive, value, or mindset, but only among conservatives for whom this is a fundamental value, not liberals. In less polarized situations, the conservative motive for ingroup dominance may not be triggered; thus, conservatives and liberals may experience this motive similarly. Hence, our first hypothesis is:

H₁: In a politically polarized situation, conservatives (vs. liberals) have a greater motive to achieve ingroup dominance, but not if the situation is less polarized.

When people are motivated to achieve ingroup dominance, they have been shown to use stereotypes, exaggerations, or other misinformation that bolsters the ingroup and/or disparages the outgroup (Maass, Ceccarelli, and Rudin 1996; Scheepers et al. 2003, 2006). For instance, during political debates and elections, ingroup-skewed misinformation often spikes (Osmundsen et al. 2021; Riley 2022). We propose that conservatives will be more prone to respond to politically polarized situations by disseminating misinformation than liberals, because their desire for ingroup dominance will be more salient. See the following hypotheses and Figure 1.

Figure 1.
Framework Relating Ideology and Polarization to Motive and Misinformation.

H₂: In a politically polarized situation, conservatives (vs. liberals) are more likely to convey ingroup-skewed political misinformation, but not if the situation is less polarized.

H₃: The effects of ideology and polarization on misinformation spread are mediated by the person's motive to achieve ingroup dominance.

Overview of Studies

We conduct six methodologically diverse studies on how ideology and polarization may affect salient motives and, thus, misinformation. Study 1 uses a ten-year PolitiFact dataset of political misinformation in social media and news media by known U.S. public figures, supplemented by Wang's (2017) content coding of each figure's political ideology and discussion topic, along with Pew Research Center (Dunn 2020; Pew Research Center 2019) survey data on discussion topic polarization. Study 2 uses a similar 16-year PolitiFact dataset with date-stamped statements (Misra 2022), combined with the Federal Reserve Bank's monthly polarization index for U.S. political news (Azzimonti 2018). Studies 3–5 are experiments that measure ideology and manipulate polarization via topic framing; in addition, they measure intent to post misinformation on social media and motive to achieve ingroup dominance. To extend Study 3's basic findings, Study 4 manipulates two types of misinformation that either supports the ingroup or attacks the outgroup. Study 5 manipulates polarization more realistically via social media newsfeeds. Study 6 analyzes nearly a century of speeches by U.S. presidents to assess whether their ideology (liberal or conservative) and polarization (election or postelection) impact their use of phrases expressing the desire to achieve ingroup dominance, consistent with our theorized mediator.

Study 1: Misinformation Triggered by Politically Polarized Discussion Topics

Method

Since 2007, an organization called PolitiFact has built an archive of political statements in social media and news media by U.S. public figures (people involved in politics, social movements, or social activism) (Wang 2017). PolitiFact provides each statement in condensed form and also arranges for it to be rated by journalists on a scale ranging from 1 = “true or accurate” to 6 = “pants on fire,” or highly misinformative. We make use of a public dataset called LIAR, which includes ten years of PolitiFact-checked statements (2007–2016), augmented by Wang (2017) to include the public figure's ideology as Republican (conservative) or Democrat (liberal) and the discussion topic.

Using the discussion topic appended to each statement by Wang (2017), combined with Pew Research Center survey data, we determine the level of polarization associated with each topic. In two surveys, Pew Research Center (Dunn 2020; Pew Research Center 2019) asked people in the United States their political opinions on 23 different topics, along with their ideology: Democrat (liberal) or Republican (conservative). We characterize 21 of these 23 topics as politically polarized because liberals and conservatives disagree significantly on them, with 2 topics less polarized due to no significant disagreement between liberals and conservatives (Web Appendix B). Comparing the 23 Pew Research Center political topics with Wang’s topics appended to the PolitiFact statements, we find matches for 36% of the statements (3,532 out of 9,837). Based on these matches, 91.22% (N = 3,222 out of 3,532) of the statements are on polarized topics, and 8.78% (N = 310) are on less polarized topics. Of these 3,532 statements, 2,016 (57%) are made by conservatives, whereas 1,516 (43%) are made by liberals.

We conduct a 2 (ideology) × 2 (polarization) ANOVA followed by pairwise t-tests. The predictor variables are ideology, with two levels (conservative vs. liberal), and polarization based on the topic discussed, with two levels (high vs. low). The dependent variable is the level of misinformativeness of the statements made based on the PolitiFact ratings (1–6).

Results

There is a main effect for ideology indicating that conservatives convey more misinformation than liberals (M_conservative = 3.36 vs. M_liberal = 2.92; F(1, 3,528) = 24.34, p < .001). This main effect for ideology replicates previous findings. There is also a main effect for polarization, as there is more misinformation given high versus low polarization (M_high = 3.30 vs. M_low = 2.98; F(1, 3,528) = 12.53, p < .001). These main effects are qualified by a two-way interaction (F(1, 3,528) = 4.22, p = .040). Conservatives are more misinformative than liberals given high polarization (M_conservative = 3.62 vs. M_liberal = 2.98; t(3,528) = 11.96, p < .001) but not low (M_conservative = 3.11 vs. M_liberal = 2.85; t(3,528) = 1.50, p = .133). This study provides initial support for our H₂, relating the predictor variables of ideology and polarization to the main outcome: misinformation (see Figure 2).

Figure 2.
Ideology and Polarization Affect Misinformation U.S. 2007–2016 (Study 1).

Discussion

Study 1 provides initial evidence that conservatives convey more political misinformation than liberals in situations of high (but not low) political polarization. However, this study has limitations due to the incomplete matching of the discussion topics with the Pew Research Center survey data on topic polarization, resulting in only 36% of the dataset being utilized. Additionally, the majority (91.22%) of statements are made in polarized situations. The relatively few statements in less polarized situations could conceivably explain the null effect for ideology for those situations. Study 2 resolves these issues by using an index of political news polarization that is reported monthly (Azzimonti 2018) along with monthly data on misinformation (Misra 2022) for the United States.

Study 2: Misinformation Triggered by Politically Polarized News

Method

Study 2 uses a similar dataset to Study 1, compiled by PolitiFact, with political statements by U.S. public figures rated on misinformativeness (1–6). But this time, we use a 16-year dataset spanning 2007–2022, augmented and made available by Misra (2022). The Misra dataset contains date stamps, whereas the Wang (2017) dataset used in Study 1 lacks date stamps. Unlike Wang’s dataset, which includes the ideology of each public figure, Misra’s does not, so we scrape individuals’ party affiliation from PolitiFact's “People and Group” page. We identify the ideology for 13,517 (64%) of the statements, 7,930 (59%) by conservatives and 5,587 (41%) by liberals, and we analyze this set of statements.

To assess polarization, we use the Federal Reserve Bank of Philadelphia's (2023) U.S. political news polarization index. The bank searches for political keywords and polarization keywords in combination, in English-language U.S. news articles archived on Factiva, a major online news aggregator. It disseminates a monthly polarization index that reflects the political topics discussed and/or the topic framings in that month's news. The index is widely used in finance and economics to examine the impact of polarization on firm, market, and government outcomes, such as the cost of equity (Pham 2019), the level of cash holdings (Cheng et al. 2018), exchange rates (Jia, Goodell, and Shen 2021), and oil prices (Apergis, Hayat, and Saeed 2021).

We calculate the political polarization trend to describe the change in polarization in the news between the date of each PolitiFact-rated statement and the same date a month prior. We use the trend because there is little consensus on what constitutes polarization when it is viewed as a static state, but considerable agreement when it is viewed as a dynamic process and its level changes discernibly, either improving or deteriorating (DiMaggio, Evans, and Bryson 1996; Fiorina and Abrams 2008). In contrast to previous studies that analyzed monthly, quarterly, or annual data (e.g., Azzimonti 2018), we use daily data and calculate the polarization trend as follows.
$\begin{aligned} Adjusted polarization for statement day (ADJ) = \\ Index m \times \frac{d}{Dm} + Index m_{p 1} \times \frac{{Dm}_{p 1} - d}{{Dm}_{p 1}}, \end{aligned}$
(1)

$\begin{aligned} Adjusted polarization for statement day in prior month (ADJP) \\ = Index m_{p 1} \times \frac{d}{{Dm}_{p 1}} + Index m_{p 2} \times \frac{{Dm}_{p 2} - d}{{Dm}_{p 2}}, \end{aligned}$
(2)

$Political polarization trend = \frac{ADJ - ADJP}{ADJP},$
(3)

where Index m refers to the polarization index in the statement month m, Index m_p1 and Index m_p2 refer to the polarization indices in the one and two months prior, respectively, d denotes the statement day within the month (1–31), Dm denotes the total number of days in the month (1–31), and Dm_p1 and Dm_p2 denote the total days in the one month and two months prior, respectively. The political polarization trend is greater than 0 when polarization increases in the news, 0 when there is no change, and smaller than 0 when polarization decreases. For example, imagine a statement made on April 1, 2020, when the polarization index is 151 in February, 59 in March, and 35 in April 2020. The trend is −.61, showing a sharp drop in political polarization from March 1, 2020, to April 1, 2020, which occurred as the COVID pandemic hit the United States.
$\begin{aligned} Adjusted & polarization on April 1, 2020 \\ = 35 \times \frac{1}{30} + 59 \times \frac{31 - 1}{31} = 58, \end{aligned}$
(4)
$\begin{aligned} Adjusted & polarization on March 1, 2020 \\ = 59 \times \frac{1}{31} + 151 \times \frac{29 - 1}{29} = 147, \end{aligned}$
(5)
$\begin{aligned} Political polarization trend = \frac{58 - 147}{147} = - .61 . \end{aligned}$
(6)
We conduct an ideology (two levels) × polarization trend (interval-scaled) analysis using ANCOVA (i.e., ANOVA with the interval polarization predictor entered similar to a covariate), followed by floodlight analysis (Spiller et al. 2013). The predictor variables are the public figure's political ideology (conservative vs. liberal) and the polarization trend (continuous) when the public figure makes the statement. The dependent variable is the PolitiFact rating of statement misinformativeness (1–6).

Results

We observe a main effect for ideology on misinformation, with conservatives spreading more misinformation than liberals (M_conservative = 3.59 vs. M_liberal = 2.94; F(1, 13,513) = 629.81, p < .001) replicating previous findings. There is no main effect for polarization (F(1, 13,513) = .21, p = .648), but there is a two-way interaction (F(1, 13,513) = 6.59, p = .010). A follow-up floodlight analysis indicates that conservatives’ statements are more misinformative than those of liberals, except when the polarization trend is declining by −.65 or more (M_conservative = 3.40 vs. M_liberal = 3.08; t(13,513) = 2.55, p = .011). These findings further support H₂ (Figure 3).

Figure 3.
Ideology and Polarization Affect Misinformation U.S. 2007–2022 (Study 2).

Discussion

Study 2 finds that when political polarization in the news intensifies, conservatives are more inclined to convey political misinformation than liberals. This ideological gap diminishes and eventually disappears with declining polarization. However, due to the nature of secondary data, we can only study correlations, not causality. In addition, the statements recorded by PolitiFact are condensed, which limits our ability to conduct textual analysis to seek motives that may lead to misinformative statements. Next, we use experiments to investigate causality and the motives that may drive conservatives to convey misinformation in polarized situations.

Study 3: Experiment on Misinformative Social Media Posts Triggered by Topic Framing

Method

Design and participants

Study 3 is a controlled experiment that uses a 2 (ideology: liberal vs. conservative) × 2 (polarization: high vs. low) between-subject design. Participants’ ideology is measured. We manipulate polarization by showing quotes from Republican (conservative) and Democratic (liberal) leaders that frame the relationship between the two parties as either cooperative or oppositional. Intent to post misinformation is the dependent variable, and motive to achieve ingroup dominance is the mediator. We recruited 283 U.S. adults to complete an Amazon Mechanical Turk (MTurk) survey, 143 liberals (Democrats) and 140 conservatives (Republicans) using a recruitment quota. Participants’ age range is 19 to 78 years, the mean age is 42, and 45.6% identify as female, 52.3% as male, and 2.1% prefer not to declare.

Procedure

We asked participants to indicate their party affiliation (Republican or Democrat) to assess their ideology (conservative or liberal, respectively). Fifteen people said that neither party affiliation describes them; we did not collect data from these participants, because we assign ingroup-skewed misinformation based on ideology. Participants were informed that the survey consists of two discrete parts that are clearly labeled, and part 1 randomly manipulates political polarization. For high polarization, participants read quotes from the existing U.S. Republican and Democratic Senate leaders, based on the leaders’ actual statements, which frame the relationship between their two parties as competitive and oppositional. For low polarization, participants read quotes by the same two leaders that frame the relationship between their two parties as cooperative and bipartisan. Both Senate leaders are older white men, and their quotes are similar in length, minimizing confounds (Web Appendix C).

Survey part 2 showed five ingroup-skewed misinformative posts in random order. The posts attack the rival party, mirroring the attack messages prevalent in politics (Johnson-Cartee and Copeland 2013). Based on actual social media posts made at the time, they are matched on length and layout to avoid confounds (Web Appendix C). Conservatives (Republicans) saw attack posts like “A Democratic Senator is under investigation for helping Russian billionaires” and “Democratic Senators are deliberately creating the global food shortage,” while liberals (Democrats) saw “A Republican Senator is under investigation for helping Russian billionaires” and “Republican Senators are deliberately creating the global food shortage.”

We measure our dependent variable by asking “How likely are you to make a Facebook post like these?” (1 = “Extremely unlikely,” and 7 = “Extremely likely”). Then, we measure the mediator, motive to achieve ingroup dominance (Scheepers et al. 2003, α = .90): “Would this post make your group stronger?,” “Would this post motivate your group?,” and “Would this post make your group better?” (1 = “Extremely unlikely,” and 7 = “Extremely likely”). Afterward, participants completed a manipulation check of polarization: “Please recall the conversation between Democrat and Republican Senate leaders you saw earlier. How much do Democrats and Republicans agree or disagree with each other on different issues?” (1 = “Strong agreement,” and 7 = “Strong disagreement”). Finally, we collected demographic information.

Results

Pretest of misinformative posts

We pretested our posts on misinformativeness and ingroup skewness (Pennycook and Rand 2019) using an independent sample of 146 MTurk participants. Participants reported their party affiliation, read the ingroup-skewed misinformative posts, and answered our questions. ANOVAs find no difference between conservatives and liberals in their perceptions of post accuracy (p = .256) or ingroup skewness (p = .750; Web Appendix C). Overall, we characterize the posts as misinformation because, on a scale with 5 being “accurate” and 3 being “unsure,” the perceived accuracy means are significantly lower than 3 for both liberals and conservatives (ps < .05). Yet the perceived accuracy means are significantly higher than 1 (“inaccurate”) for both groups (ps < .001), indicating that, on average, the posts are not viewed as definitively false. We do not know whether some people might view the posts as deliberate disinformation or a purposeful intent to deceive because we did not assess intentionality.

Manipulation check of polarization

In the main study, we conduct a manipulation check of polarization. A 2 (ideology) × 2 (polarization) ANOVA finds a main effect for polarization as expected (F(1, 279) = 100.34, p < .001), with no main effect for ideology (F(1, 279) = .12, p = .727) and no two-way interaction between ideology and polarization (F(1, 279) = .51, p = .475). Participants perceived more polarization between parties in the high-polarization situation compared with low (M_high = 6.23 vs. M_low = 4.39; t(279) = 10.03, p < .001).

Intent to post misinformation

An ANOVA on misinformation intent reveals a main effect for ideology, with conservatives expressing more intent to post misinformation than liberals (M_conservative = 2.03 vs. M_liberal = 1.59; F(1, 279) = 7.34, p = .007), mirroring past findings. There is no main effect for polarization (M_high = 1.90 vs. M_low = 1.72; F(1, 279) = 1.27, p = .262) but the expected two-way interaction (F(1, 279) = 5.94, p = .015). With high polarization, conservatives report a greater intent to post misinformation than liberals (M_conservative = 2.32 vs. M_liberal = 1.48; t(279) = 3.64, p < .001). With low polarization, they are comparable (M_conservative = 1.74 vs. M_liberal = 1.69; t(279) = .19, p = .848). H₂ is supported (Figure 4, Panel A).

Figure 4.
Ideology and Polarization Affect Intent to Post Misinformation and Motive to Achieve Ingroup Dominance (Study 3).

Motive to achieve ingroup dominance

An ANOVA on motive to achieve ingroup dominance shows a main effect for ideology, with conservatives higher on this motive than liberals (M_conservative = 2.91 vs. M_liberal = 2.47; F(1, 279) = 6.10, p = .014), but no main effect for polarization (M_high = 2.83 vs. M_low = 2.55; F(1, 279) = 2.50, p = .115). There is the expected two-way interaction (F(1, 279) = 4.18, p = .042). With high polarization, conservatives express a stronger motive for ingroup dominance than liberals (M_conservative = 3.23 vs. M_liberal = 2.43; t(279) = 3.19, p = .002). With low polarization, conservatives and liberals are comparable on this motive (M_conservative = 2.59 vs. M_liberal = 2.51; t(279) = .30, p = .763). Thus, H₁ is supported (Figure 4, Panel B).

Mediation tests

We evaluate motive to achieve ingroup dominance as a mediator using PROCESS Model 8 (Hayes 2017). The increase in misinformation by conservatives versus liberals is mediated by the motive to achieve ingroup dominance given high polarization (indirect effect = .4199, 95% CI = [.1366, .7516]), but not low (indirect effect = .0397, 95% CI = [−.1875, .2828]). The moderated mediation index comparing indirect effects for high and low polarization is significant (index = .3802, 95% CI = [.0217, .7826]). H₃ is supported.

Discussion

In this experiment, we replicate and extend the results of our two secondary data studies. Our findings indicate that when conservatives encounter a politically polarized situation, it triggers their desire for ingroup dominance, which leads them to post ingroup-skewed political misinformation. Liberals are not so triggered, and post less misinformation.

Study 4: Experiment with Replicate Misinformative Posts to Show Robustness

Method

Design and participants

Study 4 seeks to replicate the previous findings using two types of ingroup-skewed misinformative posts: those that either support one's own party or attack the rival party. We use a 2 (ideology: liberal vs. conservative) × 2 (polarization: high vs. low) × 2 (replicate misinformative posts: support vs. attack) between-subject design and our prior measures. We recruited 581 U.S. participants for an MTurk survey, 305 liberals (Democrats) and 276 conservatives (Republicans), aged 18 to 77 years (mean age = 42 years), 47.3% identifying as female, 50.6% as male, .9% as nonbinary, and 1.2% prefer not to declare.

Materials and procedure

Participants were asked to report their party affiliation as Republican or Democrat; 21 said neither, which terminated their data collection. The main survey has two parts, each clearly labeled. Part 1 randomly manipulates polarization to be high or low as in Study 3 (Web Appendix C). Part 2 shows the misinformative social media posts, five per condition. As a randomized replicate factor, conservatives (Republicans) were shown attack-outgroup posts like “Democratic Senators are all racist” or, alternatively, support-ingroup posts like “Republican Senators are never racist.” Liberals (Democrats) were shown comparable attack-outgroup posts like “Republican Senators are all racist” or, alternatively, support-ingroup posts like “Democratic Senators are never racist.” We use the Study 3 measures (intent to make similar misinformative posts [dependent variable] and motive to achieve ingroup dominance [mediator]; alpha = .94). We obtained the same polarization manipulation check and a replicate post check and collected demographics.

Results

Pretest of misinformative posts

We pretested our posts using an independent sample of 239 MTurk participants. They reported their party affiliation, read the misinformative posts, and answered questions about post accuracy and ingroup skewness. An ANOVA confirms that, as expected, political ideology and replicate post do not impact perceptions of post accuracy (ps > .231) or ingroup skewness (ps > .142; Web Appendix D). Both conservatives and liberals rated post accuracy as significantly lower than 3 (“unsure”; ps < .001) but higher than 1 (“inaccurate”; ps < .001). Both conservatives and liberals perceived the posts they were shown as ingroup-skewed regardless of whether the posts expressly supported their own party or attacked the rival party. The average ingroup-skew ratings are consistently higher than the neutral scale midpoint of 3 (ps < .001).

Manipulation check of polarization

In the main study, a 2 (ideology) × 2 (polarization) × 2 (replicate post) ANOVA on the polarization manipulation check finds the expected main effect for polarization (M_high = 6.24 vs. M_low = 4.07; F(1, 573) = 268.78, p < .001) with no main effect for ideology (p = .435) or replicate post (p = .574) and no interactions (ps > .10).

Intent to post misinformation

An ANOVA on intent to post misinformation finds a main effect for ideology, consistent with prior work (M_conservative = 2.05 vs. M_liberal = 1.75; F(1, 573) = 4.92, p = .027), but no main effect for polarization (M_high = 1.94 vs. M_low = 1.86; F(1, 573) = .40, p = .527) or replicate post (M_support = 1.98 vs. M_attack = 1.81; F(1, 573) = 1.57, p = .210). We see the expected two-way interaction between ideology and polarization (F(1, 573) = 8.23, p = .004), with no three-way interaction involving replicate post (F(1, 573) = 1.05, p = .306), so we collapse across replicate posts. Given high polarization, conservatives reported greater intent to post misinformation than liberals (M_conservative = 2.28 vs. M_liberal = 1.60; t(573) = 3.58, p < .001). Given low polarization, their intent is similar (M_conservative = 1.81 vs. M_liberal = 1.90; t(573) = .46, p = .644). These results again support H₂ (Figure 5, Panel A).

Figure 5.
Ideology and Polarization Affect Intent to Post Misinformation and Motive to Achieve Ingroup Dominance (Study 4).

Motive to achieve ingroup dominance

An ANOVA on motive to achieve ingroup dominance shows main effects for ideology (M_conservative = 2.85 vs. M_liberal = 2.51; F(1, 573) = 5.71, p = .017) and replicate post (M_support = 2.85 vs. M_attack = 2.50; F(1, 573) = 6.03, p = .014), but not polarization (M_high = 2.80 vs. M_low = 2.56; F(1, 573) = 2.80, p = .095). We see the anticipated two-way interaction between ideology and polarization (F(1, 573) = 4.01, p = .046), with no three-way interaction involving replicate post (F(1, 573) = .26, p = .611), and so we collapse across replicate posts. With high polarization, conservatives expressed a stronger motive to achieve ingroup dominance than liberals (M_conservative = 3.11 vs. M_liberal = 2.49; t(573) = 3.10, p = .002). With low polarization, conservatives and liberals were comparable on this motive (M_conservative = 2.59 vs. M_liberal = 2.53; t(573) = .28, p = .784, Figure 5, Panel B). This finding further supports H₁.

Mediation tests

Based on PROCESS Model 8 (Hayes 2017), the increase in misinformation from conservatives versus liberals is mediated by a motive to achieve ingroup dominance given high polarization (indirect effect = .4345, 95% CI = [.1660, .7190]), but not low (indirect effect = .0305, 95% CI = [−.2344, .2962]). The moderated mediation index comparing indirect effects for high and low polarization is significant (index = .4041, 95% CI = [.0252, .7953]). H₃ is supported.

Discussion

Study 4 finds the same pattern as before. In a politically polarized situation, conservatives (vs. liberals) are more motivated to attain ingroup dominance, resulting in a higher intent to post ingroup-skewed political misinformation. In a less polarized situation, conservatives and liberals are comparable. We replicate these effects using misinformation that either expressly supports the ingroup or attacks the outgroup; it does not seem to matter. In other words, the specific approach to seeking ingroup dominance appears to be inconsequential.

Study 5: Experiment with Misinformative Social Media Newsfeeds to Show Robustness

Method

Design and participants

Study 5 employs a more realistic manipulation of political polarization within a situation. People see social media newsfeeds based on real news, where the news topics and/or the topic framings are either highly polarized or less so (Brady, Crockett, and Van Bavel 2020; Kubin and Von Sikorski 2021). We use a 2 (ideology: liberal vs. conservative) × 2 (polarization: high vs. low) between-subject design and our prior measures. We recruited 288 U.S. adults for an MTurk survey, 148 liberals (Democrats) and 140 conservatives (Republicans), aged 20 to 79 years (mean 42 years), 44.4% female, 54.5% male, and 1.1% prefer not to declare.

Materials and procedure

We asked participants their party affiliation as Republican or Democrat; 12 said neither, and their data collection was terminated. Our survey consisted of two discrete parts, each clearly labeled. In part 1, participants were randomly assigned to read a newsfeed with seven articles either high in polarization (e.g., “Senate Republican slams veto threat to unleashing U.S. energy reserves”) or low in polarization (e.g., “Congress takes bipartisan steps to protect U.S. petroleum reserves”; Web Appendix E). To avoid confounds, we used articles that were similar in length that addressed comparable topics (e.g., energy reserves, budget, jobs), but the specific topics and/or their framings were polarized (e.g., veto threat) or less so (e.g., bipartisan steps). The articles are based on U.S. news stories from January 1, 2023, to February 14, 2023, which we found on the news aggregator Factiva by searching for political keywords combined with high-and low-polarization keywords (described in Web Appendix E). Thus, our operationalization of political news polarization is similar to the widely used polarization index (Azzimonti 2018).

In survey part 2, we showed participants five misinformative posts, some supporting their own party and others attacking the rival party (Web Appendix D). We use the Study 3 measures: intent to make similar misinformative posts (dependent variable) and motive to achieve ingroup dominance (mediator; α = .93). After this, participants completed a manipulation check of polarization: “Please recall the 7 U.S. political news stories you saw at the beginning of the survey. In that news, how much did Democrats and Republicans agree or disagree on different issues?” (1 = “Strong agreement,” and 7 = “Strong disagreement”). Finally, we collected demographics.

Results

Manipulation check

As expected, an ANOVA on our manipulation check of newsfeed-induced polarization shows a main effect for polarization (F(1, 284) = 43.730; M_low = 2.45 vs. M_high = 6.24, p < .001), no main effect for ideology (p = .696), and no interaction (p = .883).

Intent to post misinformation

An ANOVA on intent to post misinformation shows no main effect for ideology (M_conservative = 2.28 vs. M_liberal = 1.99; F(1, 284) = 2.35, p = .126) or polarization (M_low = 2.10 vs. M_high = 2.16; F(1, 284) = .102, p = .750) but the expected interaction (F(1, 284) = 5.31, p = .022). After exposure to the high-polarization newsfeed, conservatives reported a greater intent to post misinformation than liberals (M_conservative = 2.53 vs. M_liberal = 1.80; t(284) = 2.71, p = .007). After exposure to the low-polarization newsfeed, conservatives and liberals were comparable in their intent (M_conservative = 2.03 vs. M_liberal = 2.18; t(284) = .54, p = .586), supporting H₂ (Figure 6, Panel A).

Figure 6.
Ideology and Polarization Affect Intent to Post Misinformation and Motive to Achieve Ingroup Dominance (Study 5).

Motive to achieve ingroup dominance

An ANOVA on motive to achieve ingroup dominance finds the posited interaction (F(1, 284) = 6.96, p = .009), with no main effect for ideology (M_conservative = 2.93 vs. M_liberal = 2.83; F(1, 284) = .22, p = .643) or polarization (M_low = 2.77 vs. M_high = 2.99; F(1, 284) = 1.10, p = .293). After exposure to the high-polarization newsfeed, conservatives expressed a stronger motive for ingroup dominance than liberals (M_conservative = 3.32 vs. M_liberal = 2.67; t(284) = 2.20, p = .029). After exposure to the low-polarization newsfeed, conservatives and liberals are comparable on this motive (M_conservative = 2.54 vs. M_liberal = 3.00; t(284) = 1.54, p = .125). H₁ is supported (Figure 6, Panel B).

Mediation tests

Based on PROCESS Model 8 (Hayes 2017), the increased misinformation by conservatives versus liberals is mediated by a motive to achieve ingroup dominance given high polarization (indirect effect = .4564, 95% CI = [.0702, .8881]) but not low (indirect effect = −.3198, 95% CI = [−.7598, .0822]). The moderated mediation index comparing indirect effects for high and low polarization is significant (index = .7763, 95% CI = [.2212, 1.4013]), supporting H₃.

Discussion

Study 5 manipulates political polarization in newsfeeds similar to social media situations. Our findings indicate that a polarized newsfeed provokes conservatives into striving for ingroup dominance, which causes them to post ingroup-skewed political misinformation, while liberals do not react this way. Even conservatives do not react this way with a less polarized newsfeed.

Study 6: Motive to Achieve Ingroup Dominance in U.S. President Speeches

Method

Study 6 is a secondary data analysis of U.S. president speeches. In this study, we aim to provide further real-world evidence that, in politically polarized situations, ingroup dominance motive is elevated among conservatives but not liberals. We use a 94-year dataset (1929–2023) composed of 18,438 speeches from 16 U.S. presidents, 8 Republicans (conservatives) and 8 Democrats (liberals), from the 31^st president (Herbert Hoover) to the 46^th (Joseph R. Biden). We compare the liberal and conservative leaders’ expressions of ingroup dominance in polarized (election) and less polarized (postelection) situations (Hernández, Anduiza, and Rico 2021). Sourced from the American Presidency Project (2023), the dataset distinguishes between election speeches (i.e., campaign speeches) and postelection speeches (inaugural addresses, State of the Union speeches, farewell addresses, and major national remarks). Overall, 3,013 speeches (57.2% from Democrats, 42.8% from Republicans) occur in polarized election situations, with 15,425 (54.1% from Democrats, 45.9% from Republicans) in less polarized postelection situations.

We analyze the text of the speeches using Linguistic Inquiry and Word Count’s (LIWC-22's) dictionary for first-person plural pronouns (e.g., “we,” “us,” “our”) to identify ingroup keywords (Boyd et al. 2022; Matos and Miller 2023; Perdue et al. 1990). We find that 123 speeches (.7%) lack ingroup keywords, so our final analysis includes 18,315 speeches. We apply LIWC-22's Contextualizer to extract sentences with these ingroup keywords, along with 15 words before and after each keyword, forming up to 31-word phrases (Fridman et al. 2021). Then, using LIWC-22's dictionaries for positive and negative tonality, we calculate the overall positivity of the words in the extracted phrases in each speech as our measure of motive to achieve ingroup dominance, and aggregate the results by speech. The formula for this word count measure is:
$\begin{aligned} Motive to achieve ingroup dominance = Positive tone words \\ - negative tone words, surrounding ingroup \\ (first - person plural) words . \end{aligned}$
For statistical analysis, we use a 2 (president ideology) × 2 (polarization) ANCOVA with covariates, followed by pairwise t-tests. President ideology compares conservative (Republican) versus liberal (Democrat). Political polarization in the situation compares high polarization (election, i.e., campaign speeches) versus low polarization (postelection speeches). The dependent variable is expressed motive to achieve ingroup dominance in the speeches based on word count. We include the president ID (categorical), speech word count (interval), and overall speech tone (interval) as control variables (i.e., covariates). President ID controls for differences across presidents, while the other variables control for differences in overall speech length and valence. LIWC-22 provides the speech word counts overall and by valence. We calculate the overall speech tone as follows: (positive word percentage − negative word percentage) × speech word count.

Results

The ANCOVA finds a main effect of ideology on motive to achieve ingroup dominance, with conservative presidents expressing more ingroup dominance in their speeches than liberals (M_conservative = 55.82 vs. M_liberal = 51.35; F(1, 18,308) = 63.40, p < .001). There is also a main effect for polarization, as high polarization increases expressions of ingroup dominance (M_high = 55.75 vs. M_low = 51.42; F(1, 18,308) = 53.35, p < .001). We see the expected two-way interaction (F(1, 18,308) = 218.59, p < .001). Given high polarization, conservative presidents used 13 more words to express motive to achieve ingroup dominance than liberals (M_conservative = 62.11 vs. M_liberal = 49.40; F(1, 18,308) = 153.96, p < .001). Given low polarization, liberal presidents used four more words to express motive to achieve ingroup dominance than conservatives, which is a minor albeit statistically significant difference in the opposite direction (M_conservative = 49.54 vs. M_liberal = 53.30; F(1, 18,308) = 69.92, p < .001) (Figure 7). Overall, H₁ is supported. These results include covariates; Web Appendix F reports results without covariates, which are similar.

Figure 7.
Ideology and Polarization Affect Motive to Achieve Ingroup Dominance, U.S. Presidents’ Speeches, 1929–2023 (Study 6).

Discussion

In Study 6, we examine nearly a century of U.S. presidents’ speeches. We find that in politically polarized situations (during elections), conservative presidents use more words expressing motive to achieve ingroup dominance than liberal presidents. However, this effect diminishes and even slightly reverses in less polarized situations (postelection). We acknowledge that the use of first-person plural (e.g., “we”) in speeches could sometimes refer to the U.S. citizenry as a whole rather than a partisan ingroup (Democrats or Republicans), but the U.S. citizenry is arguably another ingroup. Thus, it appears that conservatives are motivated to attain ingroup dominance given polarization, and liberals less so, possibly regardless of the ingroup (meriting future study).

General Discussion

Summary of Findings and Theoretical Insights

The impact of political ideology on misinformation spread has attracted considerable research of late. The majority of studies show a tendency for conservatives to spread more misinformation than liberals (Allcott and Gentzkow 2017; Grinberg et al. 2019; Guess, Nagler, and Tucker 2019; Hameleers and Minihold 2022; Nikolov, Flammini, and Menczer 2021; Osmundsen et al. 2021). However, situational factors, such as political polarization, have been neglected in studies of the misinformation landscape. While research has connected polarization with misinformation, it has primarily focused on polarizing individual traits like ideological extremism (Bessi et al. 2016; Jenke 2023; Marino and Iannelli 2023; Osmundsen et al. 2021) and has overlooked the effects of polarizing situations caused by divisive political topics or their framing. Moreover, while it has been shown that conservatives value ingroup dominance more than liberals (Ordabayeva and Fernandes 2018; Pratto et al. 1994; Jost et al. 2003), this value has not been linked to their propensity to spread misinformation.

Our research enhances our understanding of when and why conservatives tend to spread more misinformation than liberals. We find that an ideological asymmetry emerges when politically polarized situations trigger conservatives’ desire for ingroup dominance. Acting on that salient desire, conservatives spread ingroup-skewed political misinformation, which is of uncertain accuracy, but not definitively false. In less polarized situations, conservatives’ desire to achieve ingroup dominance is tempered, along with their misinformation conveyance. In supplemental analyses, we explore whether a related conservative motive, the motive to prevent outgroup dominance, might also be driving their misinformation spread, but the results are not supportive of this alternate theory (Web Appendix G).

We find that even U.S. presidents are sensitive to political polarization within situations, underscoring its importance. In nearly a century of speeches, conservative compared with liberal U.S. presidents are more likely to use wording that expresses ingroup dominance, but only when situations are politically polarized (i.e., during elections; Hernández, Anduiza, and Rico 2021). We do not observe this pattern in less polarized situations (i.e., postelection).

Substantive Implications

Drivers of misinformation

Our findings indicate that politically polarized situations are a significant cause of political misinformation, not just conservatives’ tendency to propagate it. The news media, social media, political and public figures, and others contribute to political polarization, likely driven by marketplace incentives (Wilson, Parker, and Feinberg 2020). Global news revenue has declined from $180 to $126 billion in the last decade, only partially offset by digital news revenue growth from $12 to $85 billion (World Association of News Publishers 2015, 2024). To bolster revenue, news media often accentuate political polarization by using conflict-framing headlines and stories, especially in the digital arena. Instead of emphasizing the importance of political consensus in decision-making, they highlight political conflict to garner audiences (Berry and Sobieraj 2013; Kim and Zhou 2020).

Social media is also guilty of amplifying polarization and intergroup conflict (Brady, Crockett, and van Bavel 2020; Kubin and Von Sikorski 2021). Posts that go viral on social media tend to contain contentious content, which drives user engagement (Stieglitz and Dang-Xuan 2013; Fine and Hunt 2023); consequently, social media algorithms often expressly recommend contentious content (Finkel et al. 2020). Similarly in politics, stressing polarizing party differences rallies voters and increases donations (La Raja and Wiltse 2012; Leonard et al. 2021); thus, it has become a strategic political tool (Syropoulos and Leidner 2023; Wilson, Parker, and Feinberg 2020). Due to rising polarization, conservative and liberal voting in the U.S. Congress is more divided now than any other time in recorded history (DeSilver 2022).

Harms of misinformation

People are exposed to misleading content from political leaders and commentators. It has been estimated that 75% of statements made by conservative leader Trump and 41% of statements by liberal leader Biden have been mostly false or false (PolitiFact 2024a, 2024b). Perhaps as a consequence, in roughly two decades from 2001 to 2023, distrust in the U.S. government has dramatically increased from 32% to 92% among conservatives and from 48% to 75% among liberals (Pew Research Center 2023).

Misinformation leads to more negative feelings toward and stereotypes of rival groups. Currently, about 62% of conservatives and 54% of liberals report very unfavorable attitudes toward the rival party, marked increases from 1994, when only 21% of conservatives and 17% of liberals felt this way (Pew Research Center 2022). Many conservatives and liberals perceive rival party members as immoral, dishonest, unintelligent, lazy, and/or closed-minded (Pew Research Center 2022). Misinformation also deepens distrust of the news media (Pérez-Escoda et al. 2021), with 58% of conservatives and 23% of liberals distrusting it (Jurkowitz et al. 2022), which ironically promotes the consumption of lower-quality news (Mosleh and Rand 2022).

Misinformation adversely impacts democratic processes. In the year following the 2020 U.S. presidential election and accompanying misinformation about election fraud, 400 restrictive voting bills were introduced in 47 U.S. state legislatures (Brennan Center for Justice 2021). Worse, 14 states passed restrictive voting bills that, for instance, shortened the mail-in voting period, eliminated election day registration, and/or reduced ballot dropbox access. These changes have decreased voter turnout and engagement, particularly among minority voters (Fraga and Miller 2022; Ritter, Coll, and Tolbert 2024). Misinformation has also adversely affected scientists, such that Americans’ distrust in them has increased from 12% to 27% (Kennedy and Tyson 2023).

Responses to misinformation

One potential response to misinformation in the news is to enhance fact-checking to make it more instantaneous and accessible (Porter and Wood 2021). However, fact-checking organizations are constrained by limited human and financial resources. The International Fact-Checking Network (2024) reports that 59% of these organizations operate as nonprofit or academic initiatives, 68% have fewer than ten employees, 70% rely on part-time volunteers, and 84% identify fundraising as their greatest challenge. Although some fact-checking organizations have started to implement automation, deepfakes created by artificial intelligence hinder these efforts (Lee et al. 2023). Given these challenges, we recommend that fact-checkers strategically allocate more resources when situations are politically polarized (e.g., during elections). They may also want to integrate fact checks with the U.S. Federal Reserve Bank's polarization index to better understand and predict when misinformation is likely to spike.

Media literacy education can also be used to combat misinformation (Chen, Xiao, and Kumar 2023). Although 84% of U.S. adults support media literacy education as a school requirement, only 38% of these adults have received such training (Reboot Foundation 2022). A recent survey finds that 52% of high school students are persuaded by political misinformation and 90% cannot complete crucial media literacy tasks (Breakstone et al. 2021). As of 2023, 18 U.S. states have passed bills mandating media literacy education for K-12 students, up from up just 3 states in 2013 (Media Literacy Now 2024). As of 2023, California, the most populous U.S. state, requires media literacy instruction at every grade level, with a main goal being to teach students to identify misinformation (Buller 2023). Nonprofits like Media Literacy Now (2024) and the News Literacy Project (2024) provide educational resources for this purpose.

Limitations and Future Research

It would be advantageous to measure political ideology and manipulate political polarization in two separate time periods to avoid carryover effects, so we recommend that future research adopt this approach. We also suggest comparing survey platform data on ideology with people's self-reports, so any discrepancies can be noted and analyzed. Other motives that may lead to misinformation spread should be explored, such when the misinformation is entertaining or attention-getting. Research should examine whether our findings generalize to other countries and cultures, as we use data from the United States, which is economically developed and Western. In particular, research should examine whether the ideological mindset differences between conservatives and liberals in the United States hold in Eastern cultures and whether and how this may affect misinformation spread.

Previous studies on political ideology can be extended to see if polarization magnifies their effects. For instance, if situations are politically polarized, do conservatives further narrow their charitable giving to focus on ingroups (Farmer, Kidwell, and Hardesty 2020), or do they more vehemently reject fair trade products (Usslepp et al. 2022)? In addition, prior studies that have examined individual-level polarization stemming from ideological extremism should consider situational polarization. With polarization rising globally, and conservative movements growing as well, we hope our insights will help nations, communities, and individuals better prepare for the effects on misinformation spread to preserve truth, trust, and democracy.

Supplemental Material

sj-pdf-1-jmx-10.1177_00222429241264997 - Supplemental material for Political Polarization Triggers Conservatives’ Misinformation Spread to Attain Ingroup Dominance

Supplemental material, sj-pdf-1-jmx-10.1177_00222429241264997 for Political Polarization Triggers Conservatives’ Misinformation Spread to Attain Ingroup Dominance by Xiajing Zhu and Cornelia (Connie) Pechmann in Journal of Marketing

Footnotes

Acknowledgments

The authors thank Professor John Jost for his brilliant insights early in the process, and the JM review team for their incredibly detailed and constructive feedback.

Coeditor

Cait Lamberton

Associate Editor

Bob Meyer

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Ahler

Douglas J.

(2014), “Self-Fulfilling Misperceptions of Public Polarization,” Journal of Politics, 76 (3), 607–20.

Ahmed

Saifuddin

Gil-Lopez

Teresa

(2022), “Engaging with Vilifying Stereotypes: The Role of YouTube Algorithmic Use in Perpetuating Misinformation About Muslim Congresswomen,” Journalism & Mass Communication Quarterly (published online July 23), https://doi.org/10.1177/10776990221110113.

Allcott

Hunt

Gentzkow

Matthew

(2017), “Social Media and Fake News in the 2016 Election,” Journal of Economic Perspectives, 31 (2), 211–36.

American Presidency Project (2023), “Documents,” (accessed April 25, 2024), https://www.presidency.ucsb.edu/documents.

Apergis

Nicholas

Hayat

Tasawar

Saeed

Tareq

(2021), “US Partisan Conflict Uncertainty and Oil Prices,” Energy Policy, 150, 112118.

Azzimonti

Marina

(2018), “Partisan Conflict and Private Investment,” Journal of Monetary Economics, 93, 114–31.

Barnidge

Matthew

(2015), “The Role of News in Promoting Political Disagreement on Social Media,” Computers in Human Behavior, 52, 211–18.

Bavel

Jay J. Van

Rathje

Steve

Harris

Elizabeth

Robertson

Claire

Sternisko

Anni

(2021), “How Social Media Shapes Polarization,” Trends in Cognitive Sciences, 25 (11), 913–16.

Berry

Jeffrey M.

Sobieraj

Sarah

(2013), The Outrage Industry: Political Opinion Media and the New Incivility. Oxford University Press.

10.

Bessi

Alessandro

Petroni

Fabio

Vicario

Michela Del

Zollo

Fabiana

Anagnostopoulos

Aris

Scala

Antonio

Caldarelli

Guido

Quattrociocchi

Walter

(2016), “Homophily and Polarization in the Age of Misinformation,” European Physical Journal Special Topics, 225 (10), 2047–59.

11.

Boyd

Ryan L.

Ashokkumar

Ashwini

Seraj

Sarah

Pennebaker

James W.

(2022), The Development and Psychometric Properties of Liwc-22. University of Texas at Austin, 1–47.

12.

Brady

William J.

Crockett

M.J.

van Bavel

Jay J.

(2020), “The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online,” Perspectives on Psychological Science, 15 (4), 978–1010.

13.

Breakstone

Joel

Smith

Mark

Wineburg

Sam

Rapaport

Amie

Carle

Jill

Garland

Marshall

Saavedra

Anna

(2021), “Students’ Civic Online Reasoning: A National Portrait,” Educational Researcher, 50 (8), 505–15.

14.

Brennan Center for Justice (2021), “Voting Laws Roundup: May 2021,” (accessed April 25, 2024), https://www.brennancenter.org/our-work/research-reports/voting-laws-roundup-may-2021.

15.

Buller

Robin

(2023), “‘There’s Nothing More Critical’: California Makes Schools Teach Kids to Spot Fake News,” The Guardian (December 5), https://www.theguardian.com/education/2023/dec/05/california-media-literacy-class-schools-misinformation.

16.

Chen

Sijing

Xiao

Kumar

Akit

(2023), “Spread of Misinformation on Social Media: What Contributes to It and How to Combat It,” Computers in Human Behavior, 141, 107643.

17.

Cheng

Chak Hung Jack

Chiu

Ching-Wai (Jeremy)

Hankins

William B.

Stone

Anna-Leigh

(2018), “Partisan Conflict, Policy Uncertainty and Aggregate Corporate Cash Holdings,” Journal of Macroeconomics, 58, 78–90.

18.

Chinn

Sedona

Hart

P. Sol

Soroka

Stuart

(2020), “Politicization and Polarization in Climate Change News Content, 1985–2017,” Science Communication, 42 (1), 112–29.

19.

DeSilver

Drew

(2022), “The Polarization in Today’s Congress Has Roots That Go Back Decades,” Pew Research Center (March 10), https://www.pewresearch.org/short-reads/2022/03/10/the-polarization-in-todays-congress-has-roots-that-go-back-decades/.

20.

DiMaggio

Paul

Evans

John

Bryson

Bethany

(1996), “Have American’s Social Attitudes Become More Polarized?” American Journal of Sociology, 102 (3), 690–755.

21.

Duckitt

John

(2006), “Differential Effects of Right-Wing Authoritarianism and Social Dominance Orientation on Outgroup Attitudes and Their Mediation by Threat from and Competitiveness to Outgroups,” Personality and Social Psychology Bulletin, 32, 684–96.

22.

Dunn

Amina

(2020), “As The U.S. Copes with Multiple Crises, Partisans Disagree Sharply on Severity of Problems Facing the Nation,” Pew Research Center (July 14), https://www.pewresearch.org/short-reads/2020/07/14/as-the-u-s-copes-with-multiple-crises-partisans-disagree-sharply-on-severity-of-problems-facing-the-nation/.

23.

Farmer

Adam

Kidwell

Blair

Hardesty

David M

(2020), “Helping a Few a Lot or Many a Little: Political Ideology and Charitable Giving,” Journal of Consumer Psychology, 30 (4), 614–30.

24.

Federal Reserve Bank of Philadelphia (2023), “Partisan Conflict Index,” (accessed May 4, 2023), https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/partisan-conflict-index.

25.

Ferek

Katy S.

Vielkind

Jimmy

(2023), “George Santos Expelled from Congress in Historic House Vote,” Wall Street Journal (December 1), https://www.wsj.com/politics/george-santos-faces-house-expulsion-vote-after-a-year-of-mounting-allegations-0803d7db.

26.

Fine

Jeffrey A.

Hunt

Megan F.

(2023), “Negativity and Elite Message Diffusion on Social Media,” Political Behavior, 45 (3), 955–73.

27.

Finkel

Eli J.

Bail

Christopher A.

Cikara

Mina

Ditto

Peter H.

Iyengar

Shanto

Klar

Samara

Mason

Lilliana

McGrath

Mary C.

Nyhan

Brendan

Rand

David G.

Skitka

Linda J.

Tucker

Joshua A.

Van Bavel

Jay J.

Wang

Cynthia S.

Druckman

James N.

(2020), “Political Sectarianism in America,” Science, 370 (6516), 533–36.

28.

Fiorina

Morris P.

Abrams

Samuel J.

(2008), “Political Polarization in the American Public,” Annual Review of Political Science, 11 (1), 563–88.

29.

Fraga

Bernard L.

Miller

Michael G.

(2022), “Who Do Voter ID Laws Keep from Voting?” Journal of Politics, 84 (2), 1091–105.

30.

Fridman

Ilona

Fagerlin

Angela

Scherr

Karen A.

Scherer

Laura D.

Huffstetler

Hanna

Ubel

Peter A.

(2021), “Gain-Loss Framing and Patients’ Decisions: A Linguistic Examination of Information Framing in Physician–Patient Conversations,” Journal of Behavioral Medicine, 44 (1), 38–52.

31.

Graham

Jesse

Haidt

Jonathan

Nosek

Brian A.

(2009), “Liberals and Conservatives Rely on Different Sets of Moral Foundations,” Journal of Personality and Social Psychology, 96 (5), 1029–46.

32.

Grinberg

Nir

Joseph

Kenneth

Friedland

Lisa

Swire-Thompson

Briony

Lazer

David

(2019), “Fake News on Twitter During the 2016 U.S,” Presidential Election,” Science, 363 (6425), 374–78.

33.

Guess

Andy

Aslett

Kevin

Tucker

Joshua

Bonneau

Richard

Nagler

Jonathan

(2021), “Cracking Open the News Feed: Exploring What U.S. Facebook Users See and Share with Large-Scale Platform Data,” Journal of Quantitative Description: Digital Media, 1, https://doi.org/10.51685/jqd.2021.006.

34.

Guess

Andrew

Nagler

Jonathan

Tucker

Joshua

(2019), “Less Than You Think: Prevalence and Predictors of Fake News Dissemination on Facebook,” Science Advances, 5 (1), https://www.science.org/doi/10.1126/sciadv.aau4586.

35.

Haber

Jaren

Singh

Lisa

Budak

Ceren

Pasek

Josh

Balan

Meena

Callahan

Ryan

Churchill

Rob

Herren

Brandon

Kawintiranon

Kornraphop

(2021), “Research Note: Lies and Presidential Debates: How Political Misinformation Spread Across Media Streams During the 2020 Election,” Harvard Kennedy School Misinformation Review (December 17), https://doi.org/10.37016/mr-2020-84.

36.

Hameleers

Michael

Minihold

Sophie

(2022), “Constructing Discourses on (Un)Truthfulness: Attributions of Reality, Misinformation, and Disinformation by Politicians in a Comparative Social Media Setting,” Communication Research, 49 (8), 1176–99.

37.

Han

Jiyoung

Federico

Christopher M.

(2017), “Conflict-Framed News, Self-Categorization, and Partisan Polarization,” Mass Communication and Society, 20 (4), 455–80.

38.

Hayes

Andrew F.

(2017), Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford Press.

39.

Hernández

Enrique

Anduiza

Eva

Rico

Guillem

(2021), “Affective Polarization and the Salience of Elections,” Electoral Studies, 69, 102203.

40.

Hochschild

Jennifer L

Einstein

Katherine Levine

(2015), Do Facts Matter? Information and Misinformation in American Politics. University of Oklahoma Press.

41.

Hopp

Toby

Ferrucci

Patrick

Vargo

Chris J.

(2020), “Why Do People Share Ideologically Extreme, False, and Misleading Content on Social Media? A Self-Report and Trace Data–Based Analysis of Countermedia Content Dissemination on Facebook and Twitter,” Human Communication Research, 46 (4), 357–84.

42.

Horner

Christy Galletta

Galletta

Dennis

Crawford

Jennifer

Shirsat

Abhijeet

(2021), “Emotions: The Unexplored Fuel of Fake News on Social Media,” Journal of Management Information Systems, 38 (4), 1039–66.

43.

International Fact-Checking Network (2024), “State of the Fact-Checkers Report 2023,” Poynter, https://www.poynter.org/wp-content/uploads/2024/04/State-of-Fact-Checkers-2023.pdf .

44.

Janoff-Bulman

Ronnie

(2009), “To Provide or Protect: Motivational Bases of Political Liberalism and Conservatism,” Psychological Inquiry, 20 (2–3), 120–28.

45.

Janoff-Bulman

Ronnie

Carnes

Nate C.

(2013), “Surveying the Moral Landscape,” Personality and Social Psychology Review, 17 (3), 219–36.

46.

Jenke

Libby

(2023), “Affective Polarization and Misinformation Belief,” Political Behavior, 46, 825–84.

47.

Jia

Boxiang

Goodell

John W.

Shen

Dehua

(2021), “US Partisan Conflict and High-Yield Exchange Rates,” Finance Research Letters, 40, 101785.

48.

Johnson-Cartee

Karen S

Copeland

Gary

(2013), Negative Political Advertising: Coming of Age. Routledge.

49.

Jost

John T.

Federico

Christopher M.

Napier

Jaime L.

(2009), “Political Ideology: Its Structure, Functions, and Elective Affinities,” Annual Review of Psychology, 60, 307–37.

50.

Jost

John T.

Glaser

Jack

Kruglanski

Arie W.

Sulloway

Frank J.

(2003), “Political Conservatism as Motivated Social Cognition,” Psychological Bulletin, 129, 339–75.

51.

Jost

John T.

Van Der Linden

Sander

Panagopoulos

Costas

Hardin

Curtis D.

(2018), “Ideological Asymmetries in Conformity, Desire for Shared Reality, and the Spread of Misinformation,” Current Opinion in Psychology, 23, 77–83.

52.

Jurkowitz

Mark

Mitchell

Amy

Shearer

Elisa

Walker

Mason

(2022), “U.S. Media Polarization and the 2020 Election: A Nation Divided,” Pew Research Center (January 24), https://www.pewresearch.org/journalism/2020/01/24/u-s-media-polarization-and-the-2020-election-a-nation-divided/.

53.

Kaikati

Andrew M.

Torelli

Carlos J.

Winterich

Karen Page

Rodas

María A.

(2017), “Conforming Conservatives: How Salient Social Identities Can Increase Donations,” Journal of Consumer Psychology, 27 (4), 422–34.

54.

Kennedy

Brian

Tyson

Alec

(2023), “Americans’ Trust in Scientists, Positive Views of Science Continue to Decline,” Pew Research Center (November 14), https://www.pewresearch.org/science/2023/11/14/americans-trust-in-scientists-positive-views-of-science-continue-to-decline/ .

55.

Kim

Youngju

Zhou

Shuhua

(2020), “The Effects of Political Conflict News Frame on Political Polarization: A Social Identity Approach,” International Journal of Communication, 14, 937–58.

56.

Knuutila

Aleksi

Neudert

Lisa-Maria

Howard

Philip N.

(2022), “Who Is Afraid of Fake News? Modeling Risk Perceptions of Misinformation in 142 Countries,” Harvard Kennedy School Misinformation Review (April 12), https://doi.org/10.37016/mr-2020-97.

57.

Kreps

Sarah E.

Kriner

Douglas L.

(2022), “The Covid-19 Infodemic and the Efficacy of Interventions Intended to Reduce Misinformation,” Public Opinion Quarterly, 86 (1), 162–75.

58.

Kubin

Emily

von Sikorski

Christian

(2021), “The Role of (Social) Media in Political Polarization: A Systematic Review,” Annals of the International Communication Association, 45 (3), 188–206.

59.

Laloggia

John

(2018), “Republicans and Democrats Agree: They Can’t Agree on Basic Facts,” Pew Research Center (August 23), https://www.pewresearch.org/short-reads/2018/08/23/republicans-and-democrats-agree-they-cant-agree-on-basic-facts/.

60.

La Raja

Raymond J.

Wiltse

David L.

(2012), “Don’t Blame Donors for Ideological Polarization of Political Parties: Ideological Change and Stability Among Political Contributors, 1972–2008,” American Politics Research, 40 (3), 501–30.

61.

Lawson

M. Asher

Kakkar

Hemant

(2022), “Of Pandemics, Politics, and Personality: The Role of Conscientiousness and Political Ideology in the Sharing of Fake News,” Journal of Experimental Psychology: General, 151 (5), 1154–77.

62.

Lee

Sian

Xiong

Aiping

Seo

Haeseung

Lee

Dongwon

(2023), “‘Fact-Checking’ Fact Checkers: A Data-Driven Approach,” Harvard Kennedy School Misinformation Review.

63.

Leonard

Naomi Ehrich

Lipsitz

Keena

Bizyaeva

Anastasia

Franci

Alessio

Lelkes

Yphtach

(2021), “The Nonlinear Feedback Dynamics of Asymmetric Political Polarization,” Proceedings of the National Academy of Sciences, 118 (50), e2102149118.

64.

Lutzke

Lauren

Drummond

Caitlin

Slovic

Paul

Árvai

Joseph

(2019), “Priming Critical Thinking: Simple Interventions Limit the Influence of Fake News About Climate Change on Facebook,” Global Environmental Change, 58, 101964.

65.

Maass

Anne

Ceccarelli

Roberta

Rudin

Samantha

(1996), “Linguistic Intergroup Bias: Evidence for In-Group-Protective Motivation,” Journal of Personality and Social Psychology, 71 (3), 512–26.

66.

Marino

Giada

Iannelli

Laura

(2023), “Seven Years of Studying the Associations Between Political Polarization and Problematic Information: A Literature Review,” Frontiers in Sociology, 8.

67.

Matos

Yalidy

Miller

Joshua L.

(2023), “The Politics of Pronouns: How Trump Framed the Ingroup in the 2016 Presidential Election,” Politics, Groups, and Identities, 11, 507–25.

68.

McCarthy

Bill

(2021), “Misinformation and the Jan. 6 Insurrection: When ‘Patriot Warriors’ Were Fed Lies,” PolitiFact (June 30), https://www.politifact.com/article/2021/jun/30/misinformation-and-jan-6-insurrection-when-patriot/.

69.

McDonald

Kendrick

(2021), “Unreliable News Sites More Than Doubled Their Share of Social Media Engagement in 2020,” NewsGuard (accessed August 1, 2024), https://www.newsguardtech.com/special-reports/special-report-2020-engagement-analysis/.

70.

McPhetres

Jonathon

Rand

David G.

Pennycook

Gordon

(2021), “Character Deprecation in Fake News: Is It in Supply or Demand?” Group Processes & Intergroup Relations, 24 (4), 624–37.

71.

Media Literacy Now (2024), “U.S. Media Literacy Policy Report 2023,” (accessed April 25, 2024), https://medialiteracynow.org/policyreport/.

72.

Misra

Rishabh

(2022), “PolitiFact Fact Check Dataset,” Kaggle (accessed March 3, 2023), https://www.kaggle.com/datasets/rmisra/politifact-fact-check-dataset.

73.

Mosleh

Mohsen

Rand

David G.

(2022), “Measuring Exposure to Misinformation from Political Elites on Twitter,” Nature Communications, 13 (1), 7144.

74.

News Literacy Project (2024), “Training Opportunities for Individuals and Organizations,” (accessed April 25, 2024), https://newslit.org/training/.

75.

Nikolov

Dimitar

Flammini

Alessandro

Menczer

Filippo

(2021), “Right and Left, Partisanship Predicts (Asymmetric) Vulnerability to Misinformation,” Harvard Kennedy School Misinformation Review.

76.

Ordabayeva

Nailya

Fernandes

Daniel

(2018), “Better or Different? How Political Ideology Shapes Preferences for Differentiation in the Social Hierarchy,” Journal of Consumer Research, 45 (2), 227–50.

77.

Osmundsen

Mathias

Bor

Alexander

Vahlstrup

Peter Bjerregaard

Bechmann

Anja

Petersen

Michael Bang

(2021), “Partisan Polarization Is the Primary Psychological Motivation Behind Political Fake News Sharing on Twitter,” American Political Science Review, 115 (3), 1–17.

78.

Pennycook

Gordon

Rand

David G.

(2019), “Lazy, Not Biased: Susceptibility to Partisan Fake News Is Better Explained by Lack of Reasoning Than by Motivated Reasoning,” Cognition, 188, 39–50.

79.

Perdue

Charles W.

Dovidio

John F.

Gurtman

Michael B.

Tyler

Richard B.

(1990), “Us and Them: Social Categorization and the Process of Intergroup Bias,” Journal of Personality and Social Psychology, 59 (3), 475–86.

80.

Pereira

Andrea

Harris

Elizabeth

van Bavel

Jay J.

(2023), “Identity Concerns Drive Belief: The Impact of Partisan Identity on the Belief and Dissemination of True and False News,” Group Processes & Intergroup Relations, 26 (1), 24–47.

81.

Pérez-Escoda

Ana

Pedrero-Esteban

Luis Miguel

Rubio-Romero

Juana

Jiménez-Narros

Carlos

(2021), “Fake News Reaching Young People on Social Networks: Distrust Challenging Media Literacy,” Publications, 9 (2), 24.

82.

Pew Research Center (2019), “Partisans Differ on Spending for Most Programs but Agree on Veteran Benefits,” (April 11), https://www.pewresearch.org/politics/2019/04/11/little-public-support-for-reductions-in-federal-spending/ .

83.

Pew Research Center (2022), “As Partisan Hostility Grows, Signs of Frustration With the Two-Party System,” (August 9), https://www.pewresearch.org/politics/2022/08/09/as-partisan-hostility-grows-signs-of-frustration-with-the-two-party-system/ .

84.

Pew Research Center (2023), “Public Trust in Government: 1958-2023,” (June 24), https://www.pewresearch.org/politics/2023/09/19/public-trust-in-government-1958-2023/ .

85.

Pham

Anh Viet

(2019), “Political Risk and Cost of Equity: The Mediating Role of Political Connections,” Journal of Corporate Finance, 56, 64–87.

86.

PolitiFact (2024a), “Donald Trump,” (accessed August 8, 2024), https://www.politifact.com/personalities/donald-trump/ .

87.

PolitiFact (2024b), “Joe Biden,” (accessed August 1, 2024), https://www.politifact.com/personalities/joe-biden/ .

88.

Porter

Ethan

Wood

Thomas J.

(2021), “The Global Effectiveness of Fact-Checking: Evidence from Simultaneous Experiments in Argentina, Nigeria, South Africa, and the United Kingdom,” Proceedings of the National Academy of Sciences, 118 (37), e2104235118.

89.

Pratto

Felicia

Sidanius

Jim

Stallworth

Lisa M.

Malle

Bertram F.

(1994), “Social Dominance Orientation: A Personality Variable Predicting Social and Political Attitudes,” Journal of Personality and Social Psychology, 67, 741–63.

90.

Public Religion Research Institute (2019), “Fractured Nation: Widening Partisan Polarization and Key Issues in 2020 Presidential Elections,” (October 20), https://www.prri.org/research/fractured-nation-widening-partisan-polarization-and-key-issues-in-2020-presidential-elections/.

91.

Reboot Foundation (2022), “Science Fictions: Low Science Knowledge and Poor Critical Thinking Are Linked to Conspiracy Beliefs,” (accessed August 1, 2024), https://reboot-foundation.org/research/science-fictions-low-science-knowledge-poor-critical-thinking-linked-to-conspiracy-beliefs/ .

92.

Reed

Betsy

(2023), “Republican George Santos Expelled from Congress in Bipartisan Vote,” The Guardian (December 1), https://www.theguardian.com/us-news/2023/dec/01/george-santos-expelled-congress-vote.

93.

Riley

Jeffrey K.

(2022), “Angry Enough to Riot: An Analysis of In-Group Membership, Misinformation, and Violent Rhetoric on TheDonald.win Between Election Day and Inauguration,” Social Media + Society, 8 (2), https://doi.org/10.1177/20563051221109189.

94.

Ritter

Michael J.

Coll

Joseph A.

Tolbert

Caroline J.

(2024), “Can Election Administration Overcome the Effects of Restrictive State Voting Laws?” Election Law Journal: Rules, Politics, and Policy, 23 (1), 19–38.

95.

Schaeffer

Katherine

(2020), “Far More Americans See ‘Very Strong’ Partisan Conflicts Now Than in the Last Two Presidential Election Years,” Pew Research Center (March 4), https://www.pewresearch.org/short-reads/2020/03/04/far-more-americans-see-very-strong-partisan-conflicts-now-than-in-the-last-two-presidential-election-years/.

96.

Scheepers

Daan

Spears

Russell

Doosje

Bertjan

Manstead

Antony S.R.

(2003), “Two Functions of Verbal Intergroup Discrimination: Identity and Instrumental Motives as a Result of Group Identification and Threat,” Personality and Social Psychology Bulletin, 29 (5), 568–77.

97.

Scheepers

Daan

Spears

Russell

Doosje

Bertjan

Manstead

Antony S.R.

(2006), “Diversity in in-Group Bias: Structural Factors, Situational Features, and Social Functions,” Journal of Personality and Social Psychology, 90 (6), 944–60.

98.

Scheufele

Dietram A.

Krause

Nicole M.

(2019), “Science Audiences, Misinformation, and Fake News,” Proceedings of the National Academy of Sciences, 116 (16), 7662–69.

99.

Spiller

Stephen A.

Fitzsimons

Gavan J.

Lynch

John G.

McClelland

Gary H.

(2013), “Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated Regression,” Journal of Marketing Research, 50 (2), 277–88.

100.

Stieglitz

Stefan

Dang-Xuan

Linh

(2013), “Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior,” Journal of Management Information Systems, 29 (4), 217–48.

101.

Syropoulos

Stylianos

Leidner

Bernhard

(2023), “Emphasizing Similarities Between Politically Opposed Groups and Their Influence in Perceptions of the Political Opposition: Evidence from Five Experiments,” Personality and Social Psychology Bulletin (published online September 5), https://doi.org/10.1177/01461672231192384.

102.

Usslepp

Thomas

Awanis

Sandra

Hogg

Margaret K

Daryanto

Ahmad

(2022), “The Inhibitory Effect of Political Conservatism on Consumption: The Case of Fair Trade,” Journal of Business Ethics, 176 (3), 519–31.

103.

Vosoughi

Soroush

Roy

Deb

Aral

Sinan

(2018), “The Spread of True and False News Online,” Science, 359 (6380), 1146–51.

104.

Wang

William Yang

(2017), “‘Liar, Liar Pants on Fire’: A New Benchmark Dataset for Fake News Detection,” arXiv, https://doi.org/10.48550/arXiv.1705.00648.

105.

Wilson

Anne E

Parker

Victoria A

Feinberg

Matthew

(2020), “Polarization in the Contemporary Political and Media Landscape,” Current Opinion in Behavioral Sciences, Political Ideologies, 34, 223–28.

106.

Womick

Jake

Rothmund

Tobias

Azevedo

Flavio

King

Laura A.

Jost

John T.

(2019), “Group-Based Dominance and Authoritarian Aggression Predict Support for Donald Trump in the 2016 U.S. Presidential Election,” Social Psychological and Personality Science, 10 (5), 643–52.

107.

World Association of News Publishers (2015), “World Press Trends Outlook 2014-2015,” (October 5), https://wan-ifra.org/2015/10/world-press-trends-2015-report-released/ .

108.

World Association of News Publishers (2024), “World Press Trends Outlook 2023-2024,” (January), https://wan-ifra.org/insight/world-press-trends-outlook-2023-2024/ .

109.

World Economic Forum (2024), “Global Risks Report 2024,” (January 10), https://www.weforum.org/publications/global-risks-report-2024/.

110.

Yang

Yunkang

Davis

Trevor

Hindman

Matthew

(2023), “Visual Misinformation on Facebook,” Journal of Communication, 73 (4), 316–28.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

1.00 MB