The Psychology of Online Political Hostility: A ...

American Political Science Review (2021) 1–18

doi:10.1017/S0003055421000885 © The Author(s), 2021. Published by Cambridge University Press on behalf of the American PoliticalScience Association.

The Psychology of Online Political Hostility: A Comprehensive,Cross-National Test of the Mismatch HypothesisALEXANDER BOR Aarhus University, Denmark

MICHAEL BANG PETERSEN Aarhus University, Denmark

Why are online discussions about politics more hostile than offline discussions? A popular answerargues that human psychology is tailored for face-to-face interaction and people’s behaviortherefore changes for the worse in impersonal online discussions. We provide a theoretical

formalization and empirical test of this explanation: the mismatch hypothesis. We argue that mismatchesbetween human psychology and novel features of online environments could (a) change people’sbehavior, (b) create adverse selection effects, and (c) bias people’s perceptions. Across eight studies,leveraging cross-national surveys and behavioral experiments (total N = 8,434), we test the mismatchhypothesis but only find evidence for limited selection effects. Instead, hostile political discussions are theresult of status-driven individuals who are drawn to politics and are equally hostile both online and offline.Finally, we offer initial evidence that online discussions feel more hostile, in part, because the behavior ofsuch individuals is more visible online than offline.

In 2010, Mark Zuckerberg was named “Person ofthe Year” by Time magazine. In the magazine’scoverage, themission of Zuckerberg was described

in this way: “Facebook wants to populate the wilder-ness, tame the howling mob and turn the lonely, anti-social world of random chance into a friendly world”(Grossman 2010). Just a decade ago, the hope was thatcitizens would use social media to engage in civil dis-cussions about important matters. These hopes wereparticularly high regarding discussions about politics(see e.g., Dahlberg 2001) as the anonymity of theInternet was seen as finally providing people with acontext for the power-free communication emphasizedin theories of deliberative democracy.However, online discussions about politics turned

out to be nasty, brutish, and not nearly short enough.According to a recent survey, two thirds of Americanshave witnessed online harassment, and politics is one ofthe most toxic subjects on the Internet (Duggan 2017).Such findings are mirrored across Western democra-cies. For example, 80% of Danes, find that onlinediscussions are dominated by “very extremeviewpoints,” and 57% agree that some debaters are“so aggressive that you don’t know what they arecapable of” (TrygFonden 2017). The very features ofonline environments that we hoped would facilitatepeaceful deliberations are now widely believed to bea core trigger of political hostility in online discussions.There appears to be a hostility gap where online dis-cussions are felt as significantly more hostile thanoffline discussions.In this article, we provide a comprehensive investi-

gation of the potential individual-level causes of thehostility gap. We define political hostility as the use of

intimidation in political discussions and ask why peopleexperience more political hostility in online versusoffline discussions. As online forms of political engage-ment becomemore andmore widespread, this questionis of increasing importance. Mapping the causes ofonline political hostility has thus been identified as a“key question for achieving impact on onlineharassment” (Matias 2016, 1).

This article focuses on one of the most commonhypotheses informing both academic and populardiscussions of online political hostility, what we referto as the mismatch hypothesis (e.g., Baek, Wojcieszak,and Delli Carpini 2012; Cheng et al. 2017; Stein 2016).The mismatch hypothesis emphasizes the psycho-logical consequences of differences between onlineand offline contexts. In other words, it focuses onhow individuals behave differently in fundamentallysimilar political discussions online versus offline. Inone of its simplest forms, the hypothesis entails thatwhen people cannot see their discussion partner, evenotherwise agreeable individuals struggle to containtheir emotions, especially on contentious topics suchas politics. Wolchover (2012), for example, argues thatonline hostility emerges from “a perfect storm” of“virtual anonymity and thus a lack of accountability,physical distance and the medium of writing.” Or, inthe words of Cheng et al. (2017), “anyone can becomea troll.”

While such narratives are popular, there is a dearthof empirical evidence. Thus, most previous treatmentsof online hostility have exclusively focused on onlinebehavior, making it impossible to identify the uniquerole of the online context (e.g., Cheng et al. 2017;Gorrell et al. 2018). Meanwhile, none of the previousworks comparing online and offline political communi-cation has investigated hostility (Baek,Wojcieszak, andDelli Carpini 2012; Bisbee and Larson 2017; Gibsonand Cantijoch 2013). Therefore, the objective of thismanuscript is to take the first steps in the production ofa viable, empirical research program on the psycho-logical origins of online political hostility.

Alexander Bor , Postdoctoral Researcher, Department of PoliticalScience, Aarhus University, Denmark, [email protected] Bang Petersen , Professor, Department of Political Sci-ence, Aarhus University, Denmark, [email protected].

Received: July 06, 2020; revised: March 30, 2021; accepted: July 12,2021.

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To facilitate this, we offer a sweeping approach, dir-ected at drawing the overall contours of the researchlandscape and providing initial empirical evidence toguide further research. First, we place the mismatchhypothesiswithinanovel, overarching theoretical frame-work for the study of the hostility of online and offlinepolitical discussions. Second, focusing on the mismatchhypothesis, we outline the universe of possible theoret-ical mechanisms whereby the online environments canshape the psychology of political hostility by influencingeither senders or receivers of political content. Third, weleverage eight empirical studies, documenting the cross-national existence of the hostility gap and provide acomprehensive test of the proposed mechanisms. Over-all, however, these studies provide little support for themismatch hypothesis. On the sender side, we demon-strate that individuals who are hostile online are just ashostile offline and just as eager to talk about politicsonline as offline. On the receiver side, we demonstratethat there are no systematic biases in people’s percep-tions of online messages. In tandem, this suggests thatonline hostility is not because online environmentsinduce accidental failures of emotion regulation. Finally,we provide initial empirical tests of the predominantalternative to the mismatch hypothesis, which we referto as the connectivity hypothesis. Consistent with thishypothesis, we find evidence that political hostilityreflects a deliberate strategy among individuals withparticular personality traits. These individuals partici-pate intensively in political debates both online andoffline, and the hostility gap simply reflects that theironline activities are substantially more visible due to thepublic nature of online platforms.

A FRAMEWORK FOR RESEARCH ON THEHOSTILITY OF ONLINE AND OFFLINEPOLITICAL DISCUSSIONS

Social media has created a unique ecosystem charac-terized by “a personalized, quantified blend of politic-ally informative expression, news, and discussion that isseamlessly interwoven with non-political content”(Settle 2018, 15). Within this multitude of politicallyrelevant online behaviors, we focus on discussions,understood as the direct exchange of argumentsbetween a sender and one or more receivers. Discus-sions, defined in this way, occur in both online andoffline environments. As argued by Settle (2018, 66),even if “there are very marked differences betweenface-to-face conversations and the interactions thatappear [online], both capture the same fundamentalconstruct: people exchanging ideas and opinion aboutpolitics with hopes of validating their own politicalviews or informing or persuading others about theiropinions.” The notion of a hostility gap in both mediaand research takes this comparability of online andoffline political discussions as the starting point (e.g.,Baek, Wojcieszak, and Delli Carpini 2012).We contend that any framework for explaining the

hostility gap, and other potential differences betweenonline and offline behavior and perceptions, needs to

distinguish between two broad types of effects of onlineenvironments. The first class—constituting the corefocus of this article—we call mismatch effects. As weelaborate below, this class of effects imply that the“perfect storm” of novel online features (e.g., anonym-ity and rapid text-based communication) induces fleet-ing psychological changes that increase the likelihoodof certain psychological states that undermine civildiscussions (Baek, Wojcieszak, and Delli Carpini2012; Cheng et al. 2017). Simply put, when people logonline their level of empathy is reduced or they becomemore aggressive than usual.

Narratives that emphasize mismatch effects oftenreference the entire package of features that differen-tiates online versus offline discussions rather than pin-pointing a single feature at the expense of others. Toprovide an initial comprehensive examination, we fol-low suit and assess the overall differences betweenpolitical discussions that occur in online rather thanoffline environments and leave it to future research tocontrast single features.

Instead, we contrast the mismatch hypothesis withexisting research on hostility more broadly. Thus, it isnotable that while mismatch-oriented explanations ofonline (political) hostility emphasize the role of fleetingstates, research on offline hostility often emphasizes therole of stable psychological traits such as status seeking(Bartusevičius, van Leeuwen, and Petersen 2020;Petersen, Osmundsen, and Bor 2020). In this alterna-tive view, the personality of discussants matters morefor the hostility of a discussion than the platform whereit takes place.

But what can explain the hostility gap, if “people arepeople” no matter where they discuss? If antisocialpersonality is the main source of online (and offline)hostility, the hostility gap is likely to be an artefact ofmore mechanical effects of online environments’ con-nectivity. Online environments are unique in creatinglarge public forums, where hostile messages may reachthousands including many strangers, could stay access-ible perennially, and may be promoted by algorithmstuned to generate interactions (Brady, Crockett, andVan Bavel 2020; Ribeiro et al. 2019; Settle 2018). Fromthis perspective, online environments do not shape howpeople are motivated but shape what they can accom-plish given a specific set of motivations. The hostilitygap may thus emerge as a direct consequence of thelarger reach of those already motivated to be hostile.

Our focus in the present article is to thoroughly testthe role of mismatch effects by (1) comparing discus-sions in online and offline environments and (2) assess-ing the role of individual differences. Some of thesefindings point to the importance of connectivity and,accordingly, we return to and directly assess the role ofconnectivity toward the end of the article.

A MISMATCH: HUMAN PSYCHOLOGY ANDONLINE ENVIRONMENTS

To understand the potential importance of mismatcheffects for the hostility gap, we need to appreciate the

Alexander Bor and Michael Bang Petersen

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differences between offline and online interactions. Inour offline lives, we know and directly interact witharound 150 people (Hill and Dunbar 2003) and discusspolitics with only nine of them (Eveland and Hively2009). Multiple perspectives within psychological sci-ence converge on the argument that human psychology—including the mechanisms regulating aggression andhostility—is tailored to the intimate face-to-face inter-action that characterizes offline environments—forexample, emphasizing how human psychology isadapted to life in ancestral small social groups (Hilland Dunbar 2003) or how social strategies are cali-brated by direct interactions with parents and peers inearly life (Simpson and Belsky 2008). Empirically, anumber of studies have also shown that social decisionsare heavily shaped by intimate social cues such as facialexpressions (Scharlemann et al. 2001) and eye contact(Kurzban 2001). Therefore, it is likely that the mech-anisms responsible for both activating and restraininghostile responses rely on the wealth of cues available inface-to-face interactions.Online environments are different. Written inter-

actions on social media platforms or comments sectionsclash with inbuilt assumptions of human social cogni-tion in, at least, four interrelated ways. First, there is alack of the vivid social cues that are available in face-to-face interaction. Despite the abundance of emoticons,gifs, and memes, these remain only crude tools tocommunicate and understand emotions compared witha smiling face, a raised voice, or a defeated posture.Second, there is an exceptional possibility of privacyvis-à-vis discussion partners. People on the internetmay choose to remain completely anonymous or todisplay a heavily curated presentation of themselves.Meanwhile, our psychology is adapted to an environ-ment where people carry the burden of their reputationwherever they go. Third, relational mobility is signifi-cantly higher in online than in offline environments.Given the large number of potential discussion partnersonline, people can easily choose to leave one commu-nity and join another. This is not easily done in mostoffline circles. Finally, online interactions are oftensignificantly more public, with other users being ableto access the discussion even years after their occur-rence, whereas discussions that occur on the savannahor over the dinner table have significantly fewer wit-nesses.Following psychological research, we use the term

mismatch to refer to differences between a given envir-onment and the environment to which our psychologyhas adapted (Li, van Vugt, and Colarelli 2018). Priorresearch in judgment and decisionmaking suggests thatmismatches are consequential. For example, researchin management has shown that teams that primarilyinteract using computer-mediated communication aresignificantly less able to coordinate emotionally (Balteset al. 2002). Also, research in behavioral economicsshows that feelings of anonymity activate more selfishimpulses (Bohnet and Frey 1999) and that decisionmaking in situations that do not resemble face-to-faceinteractions deactivates neural circuits related to emo-tional processing (Petersen, Roepstorff, and Serritzlew

2009). Investigating how specific features contribute tosuch mismatches when it comes to online politicaldiscussions is a fruitful avenue for future research. Asthe initial step, however, we focus broadly on thepotential individual-level psychological consequencesof the totality of these mismatches.

FROM MISMATCH TO ONLINE POLITICALHOSTILITY

To provide a comprehensive basis for both present andfuture research endeavors, we seek to outline the con-tours and provide initial tests of all principal processesthrough which mismatches could create the hostilitygap. Understanding political hostility as residing inexchanges between a sender and a receiver that canoccur either online or offline, we contend that mis-matches of the online environment (1) could inducebehavioral changes in the sender for the worse, whichincrease the frequency of hostile messages online (thechange hypothesis); (2) could attract particular senders,increasing the risk that online discussions contain indi-viduals predisposed for hostility (the selection hypoth-esis); and (3) could induce perceptual changes in thereceiver, by undermining the ability to correctly per-ceive the intentions of others, raising the likelihood ofattributing hostility in online compared with offlinecontexts (the perception hypothesis). We now expandon each of these processes and the associated hypoth-eses in turn.

The Change Hypothesis

Mismatch-induced change builds off the large corpus ofresearch that shows (1) that empathy is one of the keyantidotes to aggression (e.g., Lim, Condon, and DeSteno 2015) and (2) that social and physical proximityis a key trigger of empathy (Bohnet and Frey 1999):“Face-to-face settings might generate empathy andincrease perspective taking ability to greater extentthan online settings, because interlocutors are physic-ally present and interact on an interpersonal level”(Baek, Wojcieszak, and Delli Carpini 2012, 367). In anutshell, the hostility gap may emerge from emotionregulation problems in online contexts: it is significantlymore difficult to contain hostile emotions in online thanoffline settings, especially upon discussing contentioustopics such as politics. In a paradigmatic study, Chenget al. (2017) argue that negative mood shifts and toxicdiscussions can turn most ordinary people into Internettrolls. Similarly, Coe, Kenski, and Reins (2014) findthat most hostile comments in news forums come frominfrequent users. In this view, online political hostility isan accidental failure of emotion regulation.

The Selection Hypothesis

Whereas the change hypothesis suggests that onlinepolitical hostility is largely unintended, the selectionhypothesis suggests that instrumental motivations playa significant role in it, evenmore so than offline. People

The Psychology of Online Political Hostility: A Comprehensive, Cross-National Test of the Mismatch Hypothesis

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make an active choice to opt in or out of online discus-sions (Settle and Carlson 2019). Hostile individualsmay bemore attracted to online environments, becauseaggression-based strategies can be pursued with lesscost online due to anonymity, the lack of physicalproximity, and, consequently, a low possibility of retali-ation. From this perspective, one can think of onlinediscussion environments as junk food or pornography:a culturally novel package of features that highjack thereward centers of particular individuals.This version of the mismatch hypothesis is illustrated

perfectly in a Time story on trolling: “The Internet isthe realm of the coward. These are people who are allsound and no fury” (Stein 2016). Adding some supportto this perspective, Rowe (2015) found that anonymousdiscussion forums are more hostile than nonanon-ymous ones. Also consistent with the importance ofselection, Matias (2019) found that announcing com-munity rules in online discussion forums not onlychanged the behavior of established users but alsoencouraged more civil individuals to enter these for-ums, leading to less hostility. Finally, there is someevidence that Internet trolls exhibit higher scores ondark personality traits such as sadism, suggesting thatheated online interactions are particularly attractive toonly a subset of individuals (Buckels, Trapnell, andPaulhus 2014).

The Perception Hypothesis

Both the change and the selection hypotheses focusprimarily on quantitative differences in sending mes-sages in a discussion. Meanwhile, mismatch-inducedperception focuses on qualitative differences in theeye of receivers. This version of the mismatch hypoth-esis entails that political hostility puts a heavier psycho-logical burden on receivers online than offline even ifthere is, in fact, little difference in the senders’ originalintentions.Decades of research within management demon-

strates that online interactions can limit trust and gen-erally recommends that conflicts in teams are settledface-to-face (Hertel, Geister, and Konradt 2005). Thisrecommendation reflects, in part, the danger of misun-derstandings in online communication. As concludedby Olaniran (2002, 213) “Misrepresentations appearedto be brought about in part by the medium’s lack ofnonverbal cues and the fact that sometimes receivershave the tendency to bemore serious when interpretinga message” (see also Holmes 2013). Furthermore, writ-ten online messaging is slower than verbal communi-cation and does not require people to be present for thesame discussion at the same time and place (Hesse,Werner, and Altman 1988). Consequently, online dis-cussions could drag on longer, and people may findthemselves getting involved in or unable to drop onlineinteractions they would prefer to stop. This may beexacerbated by the public nature of the online discus-sions, which drags people into discussions based ontheir group identities (Brady, Crockett, and Van Bavel2020). In general, each of these features may makepeople perceive that the resolution of conflicts is more

difficult online than offline and, by implication, con-tribute to an increased perception of online relative tooffline political hostility.

Alternatives to the Mismatch Hypothesis

The change, selection, and perception hypotheses allfall within the broad class referred to as mismatcheffects of online environments. As noted in the pro-posed framework for the study of political hostility, thehostility gap may also emerge from more mechanicaleffects wherein the connectivity and the publicity ofonline discussions in tandem make hostility more vis-ible to social media users. On this alternative account,the primary role of psychological processes is to gener-ate individual differences in who, irrespective of envir-onments, engages in hostility.

Research on the psychological roots of dominancereveals that “induc[ing] fear, through intimidation andcoercion” is a primary strategy for attaining status(Cheng et al. 2013, 105), and one of the most consistentpsychological findings is that individuals preoccupiedwith attaining higher status are much more likely tocommit aggressive and hostile acts (including homi-cide) in everyday life (Wilson and Daly 1985). Recentresearch has extended this to the political domain andfound that status-seeking (both at the individual andthe group-level) is a strong empirical predictor of sup-port for and engagement in aggression, even violence,for a political cause around the world (Bartusevičius,van Leeuwen, and Petersen 2020; see also Kalmoe2014). As summarized by Bartusevičius, van Leeuwen,and Petersen (2020) status-seeking is “a—if not the—key predictor of disruptive political behavior.”1

In short, against the mismatch hypothesis, whichemphasizes the psychological effects of online environ-ments for the emergence of online political hostility, acompeting psychological explanation entails that spe-cific individuals are hostile across all environments,both online and offline. If status-oriented traits arehighly and equally predictive of hostility across con-texts, this may imply that connectivity rather thanmismatch effects lie at the heart of the hostility gap.

As is clear from this discussion, the mismatch and theconnectivity explanations for online political hostilitymay, to some extent, be pitted against each other usingthe classical psychological distinction between states(i.e., ephemeral, context-induced motivations) versustraits (i.e., stable motivations grounded in personality)

1 These insights may seemingly contradict prominent findings byCheng et al. (2017) that situational factors trump individual differ-ences in predicting online trolling. However, it is important toremember that this study was “primarily interested in studying theeffects of mood and discussion context on the general population”(defined as people who are trolling not too often), thus it “filter[ed] banned users (of which many tend to be clearly identifiabletrolls), as well as any users who had all of their posts deleted” (11).Cheng et al’s (2017) work therefore cannot inform us how individualdifferences shape a discussant’s average likelihood to become hostile,but it offers an important reminder that situational factors affectwhich discussions turn hostile.


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(Chaplin, John, and Goldberg 1988). At the same time,it is fruitful to think of these as extreme end points on acontinuum. The change and perception hypotheseshighlight how online contexts trigger detrimental psy-chological states and thus are close to the states end ofthe continuum. Conversely, the connectivity hypothesisimplies that hostile predispositions will have similareffects in both online and offline contexts and thus liesat the traits end of the continuum. Yet, the selectionhypothesis is an example of an intermediate case,where online contexts are particularly attractive tothose with hostile personality traits and, therefore,describes states of political hostility as an interactionbetween context and traits.

OVERVIEW OF STUDIES

The causal claim of the mismatch hypothesis is that thebundle of features that distinguishes online and offlineenvironments increases experiences of political hostil-ity in online environments.While researchers often testcausal psychological arguments using laboratoryexperiments, this particular causal claim is difficult totest in such an artificial setting. Laboratory experimentscould be designed to manipulate single features ofonline versus offline interactions (e.g., anonymity ortext-based discussion). Yet, it is hard to reproduce theentire bundle of features that constitute real-worldonline interactions in ecologically valid ways (e.g., theirrepeated and public nature).Instead, we rely on other study designs: Studies 1–4

present a series of approximately representative onlinesurveys in which we observe the same individuals’experiences with political hostility in both online andoffline contexts. In thesewithin-subject designs, we alsoexamine how a primary psychological trait (specifically,individual differences in status-seeking) relates to pol-itical hostility. Our objective here is first to provideempirical evidence for the hostility gap and second totest whether people in general report changed behav-ior, select into discussions, or perceive attacks to bemore harmful online than offline.While increasingly sophisticated techniques for ana-

lyzing online behavior are being developed, we rely onself-reports to provide comparable within-subjectmeasures of online and offline behavior. Because ofthe within-subject design, bias in these measuresthreatens the validity of causal estimates only if thebias is asymmetrical between online and offline self-reports. For example, while arguably the “holier thanthou” effect (Epley and Dunning 2000) could bias self-reports of hostility downward, as long as assessmentsare equally self-serving both online and offline, it doesnot affect the validity of our tests. In Study 5, weestimate the size of a plausible source of asymmetricbias—namely, differences in social norms. Study 5 alsooffers an alternative test of the perception hypothesisrelying on a mental simulation exercise.Studies 1–5 find little evidence for the mismatch

hypothesis, but present several findings that are

broadly consistent with the connectivity hypothesis.Therefore, Studies 6–8 seek to validate these findings.Most importantly, Study 6 relies on behavioral meas-ures to validate our self-reported measures of politicalhostility and replicates the finding that highly status-driven respondents are drawn to political discussions.Study 7, in turn, asks whether people—especially thosewho are highly status driven—have the ability to care-fully calibrate the tone of their comments in onlinepolitical discussions. Finally, Study 8 offers a direct testof the connectivity hypothesis by measuring whetherpeople witness a disproportionately large share of pol-itical hostility against third parties in online contexts.

Again, it is relevant to note that the chosen designdoes not allow us to isolate the effects of single featuresof online environments. For example, it is not unlikelythat the fast-paced nature of online communicationincreases hostility, whereas its public nature decreasesit. We designed our studies to assess the average effectof the entire bundle of ecologically valid features thatcharacterizes online versus offline environments. Thepsychological effects of this “perfect storm” of featuresare the very focus of the mismatch hypothesis.

All anonymized data, scripts, and materials that arenecessary to replicate and reproduce our findings areavailable at the American Political Science ReviewDataverse (Bor and Petersen 2021).

STUDIES 1–3: WITHIN-SUBJECT TESTS OFTHE MISMATCH HYPOTHESIS

Studies 1–3 rely on original data from theUnited Statesand Denmark, respectively. These two countries con-stitute polar opposites on a number of variables rele-vant for our investigation. The United States is a high-polarization, high-conflict, low-trust, low-participationcountry, whereas Denmark is a low-polarization, low-conflict, high-trust, high-participation country (Nelsonand Shavitt 2002). Exploiting these variations, ouranalysis follows a most different systems design logicand argues that—conditional on finding similar trendsin both countries—our results should generalize toother advanced Western democracies.

Original online survey data bring a number ofimportant benefits to our analysis. First, we couldinterview approximately representative samples ofInternet users in theUnited States andDenmark. Study2 (and Study 4, below) further improve the quality ofour samples by screening out respondents who have nodirect experience with following or participating inpolitical discussions (either online or offline).

While studying various subpopulations (e.g., Twitteror Reddit users) is obviously important, we must relyon diverse samples to answer questions about the socialprevalence of hostile behavior and the perceptions ofthese environments. Thus, our data are not bounded toany single platform and offer an overview of citizens’experiences, whether they are active on Facebook,4chan, or somewhere else. Second, this feature alsocircumvents a primary challenge of studying hostilityon social media platforms such as Twitter, where


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hostile messages violate the rules of conduct and, thus,are deleted. Third, with surveys, we can rely on psy-chometrically validated personality measures, whichprovide nuanced measures of complex psychologicalconstructs (e.g., status-driven risk taking).

Prediction

The notion of the hostility gap contends that onlinepolitical discussions are perceived as more hostile thanoffline political discussions. This prediction can bedirectly tested by measuring people’s perceptions ofthe average level of hostility in online and offlinediscussions.To test the relevance of mismatch-induced change,

we focus on the most basic implication: there should bean asymmetry between hostility in online and offlinepolitical debates. People who are seldom hostile offlinecould still send hostile messages online, but not theother way around. We add nuance to this basic predic-tion by considering the role of status-seeking motiv-ations. The connectivity hypothesis suggests that suchindividual differences consistently predict hostilityacross both online and offline contexts. In contrast, ifonline hostility is a result of mismatch-induced failuresin emotion regulation, we would expect the relevanceof instrumental, status-oriented concerns to decrease inonline compared with offline acts of hostility.Mismatch-induced selection implies that people pre-

disposed for hostility intentionally select into politicaldiscussions of politics in online rather than offlineenvironments in order to indulge in hostile debates.To test the selection hypothesis, we focus on the mostbasic observable implication: individuals who aremotivated to engage in hostility—as captured by indi-vidual differences in status seeking—are more likely toengage in online than in offline political discussions.Finally, to test the mismatch-induced perception

hypothesis, we test whether people are more likely tobecome entangled in futile, draining, and frustratingdebates in online contexts than in offline contexts. InStudy 5, we turn to an even more basic test of theperception hypothesis: overattribution of hostility.

Methods and Materials

We collected data through YouGov and Lucid andfielded online surveys to approximately nationally rep-resentative samples of Americans and Danes. Theseagencies sample from a large subject pool and employquota sampling to match population characteristics onage, gender, education, and region in both countriesand also on race in the United States. For more infor-mation on our samples, see online appendix B.Study 1 is an American sample of N = 1,515 testing

the change and selection hypotheses using a within-subject design. Study 2 is a Danish sample testing allthree hypotheses using a within-subject design. A totalof 1,434 people participated in Study 2, but our focalquestions comparing online and offline behavior andexperiences were presented only to the 1,041 respond-ents who reported at least minimal experience with

following or participating in political discussions in aprescreening question. Finally, Study 3 is a preregis-tered follow-up study in the United States (N = 998)testing the change and perception hypotheses, in a split-ballot question-order experiment allowing both within-and between-subject comparisons of online and offlinehostility levels, mitigating potential concerns aboutcommon methods bias. The preregistration is availableat https://aspredicted.org/3ny87.pdf.

To test the hostility gap hypothesis, we created a 0–1additive index of perceived hostility of discussionsonline and offline by rescaling and averaging overratings for whether these discussions are perceived tobe aggressive, uncivil, and hostile. (Online appendix Areports full question wordings for all survey measuresin our manuscript).

Our main dependent variable for testing the changehypothesis is self-reported hostility in online and offlinediscussions. In designing a novel battery of questions totap into these constructs, we employed best practicesfrom survey research on cyber bullying (Griezel et al.2012) and online political behavior (Guess et al. 2019).We listed activities constituting important archetypes ofhostile behavior (e.g., making “comments that could betaken as offensive or aggressive”). For the online bat-tery, we also added two items, more specific to onlineinteractions (e.g., “getting blocked or banned from awebsite for violating its guidelines”).2 We asked parti-cipants to indicate how often these things happen tothem on a scale from “Never” to “Several times a day.”To establish within-subject comparisons in the twocontexts, we repeat the same questions twice, clearlydistinguishing whether the questions concern “face-to-face political discussions” or “occur on the Internet,including social media and comments sections.”3 InStudies 2 and 3, we added several minor improvementsto the scale to increase measurement validity.4

2 Note that our measures provide a lenient test of the change hypoth-esis by including items that measure “regretted” and online-specificbehavior. Dropping these items does not change the conclusions ofour analysis.3 Our assessment of the satisfactory validity of our novel politicalhostility scale rests on three legs. First, in Studies 1 and 2, we includeda psychometrically validated measurement of trait aggression(Diamond andMagaletta 2006). In both samples, participants scoringhigher on trait aggression are also more likely to report politicalhostility (S1 USA online r = 0.41, offline r = 0.42; S2 Denmark onliner = 0.46, offline r = 0.52), providing evidence of convergent validity.Second, in a recent study, Rasmussen et al. (2021) analyze behavioraldata from Twitter and shows that participants with higher scores onour online political hostility scale post more tweets labeled as polit-ically hateful, toxic, or highly negative by various machine learningalgorithms (PerspectiveAPI toxicity scores r= 0.30, AFINNnegativesentiment scores r = 0.25, Political hate word embedding scores r =0.33). Finally, our own behavioral experiment indicates that peoplewho report higher online political hostility are prone to write slightlymore hostile comments, even in an artificial one-shot online experi-ment (Cohen’s d = 0.3; see more details in Study 6).4 Specifically, we followed the recommendations byGuess (2015), weadded a sentence asking participants to think specifically about thepast 30 days, thereby reducing measurement error due to asymmet-ries in memory. Also, we added a final item to the online hostilitybattery stating, “I had a difficult time tempering my emotions” to


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In Studies 1 and 2, we asked the online questionsbefore the offline questions. In Study 3, we combinedthe within-subject design with a between-subjectdesign, randomizing the order of the question batteries.In all studies, we average over all items to form twoindices called offline hostility and online hostility.Afterrescaling, the two indices have a theoretical range from0 to 1. We report descriptive and scale-reliability stat-istics for all indices in online appendix B.To investigate the selection hypothesis, we adapt a

measure from Valenzuela, Kim, and Gil de Zuniga(2012), tapping into the frequency of political conver-sations with members of various groups: family andfriends, coworkers and acquaintances, strangers,people with whom they agree, and people with whomthey disagree. Again, we repeat these questions twice,first for offline discussions and then for online discus-sions.We form two reliable indices, whichwe refer to astalking about politics online and offline.Finally, to test the perception hypothesis, we

designed eight new items tapping into people’s percep-tions about the severity of political conflicts online andoffline. Five items concern whether people feel theybecome “involved in discussions they do not feel likehaving,” whether they “continue a discussion eventhough they do not enjoy it,” and so on. Meanwhile,three items focus on the resolution of conflicts, address-ing the length of discussions, the ease with which theyare discontinued, and finally whether offenses are fol-lowed by apologies. We report the results for a scalecombining all items.Our main independent variable is status-driven risk

taking (SDRT), a concept developed to tap into com-petitive risk taking. It measures individual differencesin the “tendency to seek and accept great risks … inpursuit of great rewards involving material wealth orsocial standing” (Ashton et al. 2010, 735). Respondentsrate their agreement with the items on a seven-pointscale. All items are averaged to form a single status-driven risk taking index. As alternative measures ofhostile personality, we also included the Difficulties inEmotion Regulation Scale (Gratz and Roemer 2004)and a short version of the Buss-Perry AggressionQuestionnaire in our surveys (Diamond andMagaletta2006). Online appendix D reports results with thesemeasures, replicating and extending the findings dis-cussed below.To test our predictions, we employ simple OLS

regressions. Unless otherwise noted, our models adjust(or “control”) for basic demographic covariates: age,gender, education, and income in both countries; astandard seven-point partisan identity scale in the US;a three-level (red-block, blue-block, or neither) parti-sanship variable in Denmark; and also an indicator foridentifying as white in the United States. All variablesare scaled to 0–1. Although these OLS regressionmodels are effective tools for estimating partial correl-ations between our variables, they do not directly allow

us to contrast online and offline political discussions.Formal tests for the statistical significance of the differ-ences in coefficients rely on structural equationmodels,reported in detail in online appendix D.

Results

Is there a hostility gap? Yes. Figure 1 depicts thenegativity of online (dark gray) and offline (light gray)discussions. Respondents in both countries rate onlinediscussions—USA: M (SD) = 0.57 (0.34), Denmark: M(SD) = 0.65 (0.22)—much more negatively than offlinediscussions—USA: M (SD) = 0.38 (0.29), Denmark: M(SD) = 0.34 (0.22), contributing to a substantively largeand statistically significant difference between the twoplatforms—USA: ΔM = 0.19, t(1514) = 23.9, p < 0.001,Cohen’s d = 0.61; Denmark: ΔM= 0.32, t(978) = 34.8, p< 0.001, Cohen’s d = 1.1.5

Do respondents report more online than offline hos-tility? No. Figure 2 below reports predicted levels ofonline hostility as a function of offline hostility (thickline) along with 95% confidence intervals (gray shade)and the observed values for each respondent ( jitteredpoints). These models omit demographic controls inorder for the intercept to correspond to no offlinehostility. The findings reveal a number of importantobservations.

First, we see relatively low levels of hostility in bothonline—S1US:M (SD)= 0.07 (0.17), S2DK:M (SD)=0.06 (0.16), S3 US: M (SD) = 0.13 (0.21)—and offlinesettings—S1 US: M (SD) = 0.07 (0.17), S2 DK: M(SD) = 0.07 (0.16), S3 US: M (SD) = 0.16 (0.21). Putdifferently, between one third (S3US offline) and threequarters (S2 DK online) of our samples report noinstances of political hostility. Second, online and off-line hostility are strongly related in both the UnitedStates and Denmark. Indeed, offline hostility aloneexplains 64% to 79% of the variance in online hostility.Third, to the extent that we see any asymmetry betweenthe two forms of hostility, it is toward more offlinehostility among highly hostile individuals. The inter-cepts in all models are very close to zero (S1 US: α =

0.006, p < 0.01; S2 DK: α = 0.003, n.s.; S3 US: α = 0.008,n.s.). As additional robustness checks, we performpaired t-tests and equivalence tests for all samples(online appendix D2). We also replicate our resultswith between-subject comparisons in the split-ballotdesign we implemented in Study 3 by randomizingthe order of the question batteries (online appendixD3). All our models indicate that it is unlikely that thebehavior of a meaningful part of our sample changesfor the worse in online political discussions. An objec-tion in this respect could be that these analyses primar-ily address the frequency of online and offline hostilitybut not whether the hostility is qualitatively worse in

further increase the scale’s sensitivity to minor asymmetries betweenonline and offline hostile behavior.

5 We also asked participants to rate the discussions on three positiveitems: peaceful, respectful, and constructive. As the six items do notform a single reliable scale, we report positive items separately inonline appendix, section D. The results closely mirror the patternsreported here.


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online contexts. We address this objection when testingthe perception hypothesis below.Is status-driven risk taking a less important correlate

of online than offline hostility? No. The associationbetween political hostility and status drive remainsconstant no matter which environment we look at(US Offline: β = 0.29, US Online: β = 0.29; DenmarkOffline: β = 0.44, Denmark Online: β = 0.40, all ps< 0.001). This pattern is obvious from Figure 3: Thepredicted hostility sharply increases with higher valuesof status-driven risk taking, but the predictions arecompletely overlapping for online and offline environ-ments (US: Contrast = 0.004, p = 0.77, DK: Contrast =−0.03, p = 0.12).Are respondents higher on status-driven risk taking

selecting into online as opposed to offline political

discussions? To some extent. Specifically, we regressthe two indices for talking about politics in online andoffline environments on status-driven risk taking,adjusting for demographic controls. Under the selec-tion hypothesis, we would expect that people higher onstatus-driven risk taking prefer online discussions morethan offline discussions. Figure 4 presents predictedvalues of talking about politics online (dark gray) andoffline (light gray) across the range of observed valuesof status-driven risk taking, keeping demographic cov-ariates at their mean. First, it appears that status-drivenrisk taking is correlated with general interest in politicaldiscussions—all lines have positive slopes (for similarfindings, see Sydnor 2019). However, we see no evi-dence that highly status-driven individuals preferonline interactions. Indeed, at the maximum of the

FIGURE 1. Distribution of Perceived Negativity of Online and Offline Discussions in the United Statesand Denmark

FIGURE 2. Relationship between Offline (x) and Online (y) Political Hostility


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scale, respondents report similar levels of discussionfrequency.However, unlike with the hostility measures, the two

environments are not exactly similar. At lower levelsof status-driven risk taking, peaceful respondentsreport talking more about politics offline than online.Formally, this is evident in the substantial differencebetween the intercepts of our two sets of models,reflecting the levels of online and offline politicaldiscussions at the lower end of status-driven risktaking (US Online: α = 0.16, US Offline: α = 0.21,Contrast = −0.05, p < 0.1; DK Online: α = 0.08, DKOffline: α = 0.17, Contrast = −0.09, p < 0.001). This

difference translates to a 6–7-percentage-point increasein the chance that one discusses politics with someoneabove the median of the status-driven risk-taking scale(ps < 0.01; see details in online appendix D4).6

FIGURE 3. Political Hostility as a Function of Status-Driven Risk Taking

FIGURE 4. Talking about Politics as a Function of Status-Driven Risk Taking

6 One interpretation of the selection effect is that peaceful people aredriven away by the hostility of online discussions. If this were the case,they would be most likely to select out of online discussions mostlikely to turn hostile: discussions with strangers and those with whomthey disagree (Settle and Carlson 2019). However, Figure D5 in theonline appendix shows that they never discussed politics with theseindividuals to begin with. Instead, peaceful individuals mostly discuss


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Are political conflicts perceived to be more severeonline than offline? No. To test the perception hypoth-esis, we investigate perceived differences in the severityand resolution of conflicts in offline and online discus-sions, relying on the novel scale introduced above andincluded in the Danish survey (Study 2) and the secondUS survey (Study 3). Against the perception hypoth-esis, we find that conflicts are perceived as more orequally severe in offline compared with online envir-onments (US Offline: M = 0.42, US Online: M = 0.42,ΔM = 0.002, n.s.; DK Offline: M = 0.35, DK Online: M= 0.32, ΔM = −0.04, p < 0.001). Equivalence testsprobing for the smallest effect size of interest(Cohen’s d = 0.1) show that these effects are unlikely,assuming the perception hypothesis was true (ts > 2.2,ps < 0.05). Importantly, the finding that online discus-sions are not experienced as more severe also speaksagainst a quality-oriented version of the changehypothesis, which claims that the primary differencebetween online and offline hostility is not how often ithappens but how severe it is when it happens.These findings might seem paradoxical given the

documented existence of the hostility gap. However,it is crucial to note that the evidence for the hostility gapreflects assessments of the overall discussion climates(with respect to their aggression, incivility, and hostil-ity), whereas the perception questions narrowly relateto assessments of the discussions in which the partici-pants themselves participate. Furthermore, the presentfindings are consistent with those from some previousresearch. For example, in a series of qualitative inter-views, a group of feminist political activists stated thatthey find social media “a relatively safer and easierspace to engage in feminist discussions than in… offlinecontexts” (Mendes, Ringrose, and Keller 2018, 243).Face-to-face disagreements might not only entailgreater repercussions but also bombard people with aplethora of nonverbal cues signaling the discussionpartner’s anger.

STUDY 4: REPLICATION ANDEXTENSIONOFWITHIN-SUBJECT TESTS

The primary ambition of Study 4 is to test the changehypothesis on a broader set of hostile political behav-iors. The scale developed for Studies 1–3 sought tostrike a balance between the most severe forms ofpolitical hostility (motivating our inquiry) and milderand more pervasive forms of hostility. Yet, in the twohighest quality samples (Studies 1 and 2), about twothirds of respondents claim to be innocent of politicalhostility, raising concerns that this drives the highcorrelations. Here, we measure political hostility with

a more comprehensive list of items, spanning the entirerange of political hostility from offensive jokes to trueharassment. Study 4 also seeks to replicate the selectionfindings fromStudies 1 and 2 that were ignored in Study3 due to space limitations. Unless otherwise noted, allpredictions and tests of Study 4 are preregistered athttps://aspredicted.org/3ah9y.pdf.

Predictions

Our predictions mirror the analyses described in Stud-ies 1–3. Importantly, at this point, the theoreticalexpectations of the mismatch hypothesis and ourexpectations based on previous evidence diverge.Against the mismatch-induced change hypothesis butconsistent with the assumptions underlying the con-nectivity hypothesis, we predicted to find (1) no moreself-reported hostility in online (vs. offline) politicaldiscussions, (2) a very high (r > 0.75) correlationbetween self-reported online and offline political hos-tility, and (3) no evidence that status-driven risk takingis a better predictor of online than offline politicalhostility. Similarly, against the mismatch induced selec-tion hypothesis, we predicted (4) that respondents highin status-driven risk taking are not more likely toparticipate in online than in offline political discussions.Given the findings of Studies 1 and 2, we finally pre-dicted (5) that respondents low in status-driven risk-taking select out of online (but not offline) politicaldiscussions.


A total of 1,317 American adults were interviewed byYouGov and selected via quota sampling to ensureresemblance of the population. Consistent with Study2, we screened out participants who never participate inor follow political discussions online or offline, leavingus with a final N of 770. To self-reported politicalhostility, we include the same question as before butadd a broader set of eight items ranging from “I madefun of my political opponents” to “I threatened orharassed my political opponents” (see OA Section A)for complete battery). We measure political participa-tion with the same question and items as in Study 1 and2. As in Study 3, we implemented a question orderexperiment, where participants randomly encounteredeither the block pertaining online or offline behaviorfirst.7

Results

Do people self-report more hostility in online politicaldiscussions than in offline political discussions?No. We

politics offline with friends and people with whom they agree, and thedrop we see in online discussions is also driven by friends andsimilarly minded people. These findings are more in line with analternative interpretation that the selection effect is driven by apreference among nonhostile people to avoid all online politicaldiscussions rather than hostile political discussions specifically.

7 Each block was introduced with a short clarification on how wedefine online (offline) discussions. Because of the realities of theCOVID-19 pandemic, we defined online discussions as “text-based”communications on the Internet and offline discussions as “discus-sions that occur in situations where you could hear and see the otherperson(s)” thereby including also video calls.


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find that respondents self-report equal levels of hostil-ity online and offline—Online M (SD) = 0.17 (0.21),Offline M (SD) = 0.17 (0.20), ΔM = 0.00, t(769) = 0.24,n.s. With the revised scale, only 22% and 18% ofrespondents report absolutely no political hostilityacross online and offline discussions, respectively. Inother words, it appears that most people who partici-pate or follow political discussions occasionally engagein (milder forms of) incivility.Is there a very high correlation between self-reported

online and offline political hostility within individuals?Yes (see Figure 5). We find that an individual's offlinepolitical hostility is highly predictive of their onlinepolitical hostility (Pearson’s r = 0.84).Is status-driven risk taking a better predictor of online

versus offline political hostility? No. The associationbetween status drive and hostility is identical acrossthe two contexts (Offline: β= 0.52,Online: β= 0.51, ps <0.001).Are respondents with high status-driven risk taking

more likely to participate in online than offline politicaldiscussions? Are respondents with low status-driven risktaking selecting out of online political discussions? Wefind no evidence for either. Against the mismatchhypothesis and our previous findings (Studies 1 and2), we find identical levels of participation across offlineand online contexts along the entire range of SDRT.Participants low on SDRT participate just as little bothonline (α = 0.16, p < 0.001) and offline (α = 0.18, p <0.001). Meanwhile, the effect of SDRT is large andpositive across both contexts (Offline: β = 0.35, Online:β = 0.38, ps < 0.001).To summarize, Study 4 replicated previous results

regarding the change hypothesis with a novel bat-tery spanning a broader and more comprehensivelist of hostile behaviors. Meanwhile, we failed to

replicate the selection effects found in the previoustwo studies.8

STUDY 5: VIGNETTE EXPERIMENT

The evidence against the mismatch hypothesis gener-ated by Studies 1–4 relied on within-subject tests. Thisindividual differences approach may raise concerns,especially regarding the perception hypothesis, as itignores the most straightforward implication of theperception hypothesis: the same message feels morehostile when it is uttered in an online (vs. an offline)setting. Study 5 was designed to provide a direct,experimental test of this.

In addition, and with implications for especially thechange hypothesis, the within-subject tests assume thatself-reported hostility online and offline are directlycomparable. Yet, a possible concern is whether peoplehave different norms about online and offline conver-sations. If this were the case, people could make object-ively more hostile statements online than offline butself-report equal levels of hostility because their onlinemessages feel just as appropriate for the context, des-pite their content. The effects of such potential normdifferences, however, are not straightforward becausenorm asymmetries also couldmake peoplemorewillingto admit to a hostile behavior online, where it is not anorm violation. To assess this potential complicating

FIGURE 5. Preregistered Replication of High Correlation between Offline (x) and Online (y) Hostility,Relying on an Extended Hostility Battery from Study 4

8 Given that Study 4 was collected during the COVID-19 pandemic,we cannot rule out that the failure to replicate this effect reflectschanges in respondents’ discussion patterns as a response to thepandemic.


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factor, Study 5 also tests whether offensive statementsare considered more appropriate online than offline.

Predictions

To test the perception hypothesis, we predicted thathostile messages are considered more offensive inonline (vs. offline) contexts. Importantly, against theperception hypothesis and consistent with the connect-ivity hypothesis and the previous findings, we expectthat we will not find evidence for this hypothesis.To assess the potential existence of a norm asym-

metry, we use a distinction between injunctive anddescriptive social norms (Gerber and Rogers 2009).Injunctive norms refer to behavior people perceive tobe normatively appropriate or desirable. If there is anorm asymmetry in terms of injunctive norms, weexpect that hostile messages would be considered lessinappropriate in online (vs. offline) contexts, given thestrong norms of civility in regular face-to-face discus-sions. Descriptive social norms refer to what otherpeople actually do. While hostility is relatively rare inoffline discussions, the hostility gap implies that it ismuchmore frequent online. If there is norm asymmetryin terms of descriptive norms, we would expect thathostile messages are considered less rare in online(vs. offline) contexts.


To maximize internal validity, Study 5 relies on thepopular experimental vignette methodology, invitingparticipants to imagine themselves in a situationdescribed in a carefully manipulated yet highly realisticscenario. Social and organizational psychologists havefound that such mental simulation exercises offer agood approximation of real experience (Aguinis andBradley 2014). We presented participants with fourhostile messages on controversial social topics (immi-gration, abortion, COVID-19, Capitol siege) balancedon ideological stance (the former two being conserva-tive, the latter two being liberal). The order of themessages was randomized. We manipulated the con-text in which the message was uttered in a 2 (onlinevs. offline) � 2 (private vs. public) within-participantdesign. As our predictions highlight our main focus isthe online versus offline effect, but we included thepublic versus private manipulation for exploratory pur-poses (see OA Section E) and to reduce experimenterdemand effects. As an illustration, the immigrationmessage read (highlighting the manipulated text withbold) as follows:

Imagine that you participate in a political discussion at adinner party with 5 other people/public town hall meeting/private chat group with 5 other people/public Internetforum. The discussion turns to the topic of the siege ofthe US Capitol. Someone makes the following comment:“Folks who think that the current levels of immigration aresustainable are stupid and irresponsible. Crime andunemployment due to immigration hurt hypocrite Demo-crats as much as everyone else. Wicked and sadistic

immigrants harass innocent people. They should respectthe culture of people living in this country more.”

Participants were asked three questions after eachmessage: (1) how offensive they found the message,(2) how appropriate or inappropriate the commentwould be in the given context, and (3) how commonor rare it is that such a comment is made in the givencontext. Each question was answered on a 1–7 scalewith “not offensive at all” versus “extremelyoffensive,” “perfectly appropriate” versus “extremelyinappropriate,” and “very common” versus “very rare”employed as anchors.

Study 5 was fielded on the same sample as Study 4;however, importantly, it included all respondents, irre-spective of whether they ever participate or followpolitical discussions (N = 1,317 US Americans).Because each respondent answered these questionsfour times, our analyses rely on 5,268 observations.We regress offensiveness, inappropriateness, and rarityon an indicator for online context while including fixedeffects for respondents and stories to account for thewithin-respondent design. We preregistered all ourdecisions at https://aspredicted.org/p2cy6.pdf.

Results

Are hostilemessages consideredmore offensive in online(vs. offline) contexts? No. Consistent with previousfindings, saying something online is not perceived tobe inherently more offensive (β = −0.01, n.s.). Judgingfrom the 95% confidence interval, even a vanishinglysmall effect (>0.006) is inconsistent with our data.

Are hostile messages considered less inappropriate inonline (vs. offline) contexts? Yes, although the differ-ence is very small (β = −0.03, p < 0.001).

Are hostile messages considered less rare in online(vs. offline) contexts?Yes, although thedifference is evensmaller than for inappropriateness (β=−0.02, p< 0.001).

Overall, consistent with our previous findings, we findno evidence for the perception hypothesis.Whilewe findsomeevidence for normasymmetries, these appear to besubstantially small. To assess whether they could exert abias on the self-reports of political hostility, we ran posthoc analyses using the difference between the perceivedinappropriateness of online versus offline comments inStudy 5 as an individual differencemeasure of perceivednorm asymmetry (higher values indicate that therespondent believes it is more permissible to be hostileonline).We findnoevidence that this perceived inappro-priateness gap is associatedwith being hostile online, nordoes it moderate the relationship between offline andonline hostility. To summarize, we find it unlikely thatthe self-reported hostility scales are differentially biasedby norm asymmetries.

STUDIES 6–7: BEHAVIORAL EXPERIMENTS

Studies 1–5 find little evidence for the mismatchhypothesis but are consistent with the primary


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assumption underlying the connectivity hypothesis:That individual-level aggressive traits drive politicalhostility equally both online and offline. In the nexttwo studies, we test the robustness of and addnuance to these findings, relying on original behavioralexperiments.To increase the measurement validity of our conclu-

sions, Study 6 asks whether our self-reported hostilityscale correlates with observed hostility in a politicaldiscussion. Meanwhile, Study 7 seeks to discernbetween different explanations for the behavior ofthose high in status-driven risk taking. The literatureon status-driven hostility suggests that this behavior isfueled by strategic motivations to increase personal orgroup-based status (Petersen, Osmundsen, and Bor2020). At the same time, however, the present evidencecannot rule out that the behavior of those predisposedfor hostility reflects a form of mismatch-induced per-ception, where their hostility is grounded in an inabilityto correctly perceive the tone of messages in politicaldiscussions. Study 7 provides a direct test of this.Both experiments emulate discussions on Facebook

using between-subject experiments and behavioralmeasures of hostility. Participants read aFacebook poston immigration, whichwasmanipulated in a 2 (position:pro-immigration or anti-immigration) � 8 (hostility)between-subjects factorial design. We chose immigra-tion to the United States because it is hotly debatedpolicy issue.

Predictions

Themain ambition for Study 6 is to validate our originalself-reported hostility measure. Therefore, we expectthat participants who self-report being hostile moreoften in online political discussions will also write morehostile messages in our experiment.Study 7 asks whether respondents, and particularly

respondents high in status-driven risk taking, are ableto match the tone of political messages following asimple written instruction to do so. Study 7 thus pro-vides a direct test of whether some respondents areincapable of correctly perceiving the tone of politicaldiscussions. While Studies 1–4 leave little doubt thatonline environments do not contain systematicallymore cues inducing “accidental” hostility, we offeredno evidence for the argument that hostility is strategic.9


A total of 2,137 and 2,089 participants living in theUnited States were recruited on Amazon’s MechanicalTurk for Studies 6 and 7, respectively; 206 (Study 6) and449 (Study 7) participants were excluded from thestudy for failing simple comprehension checks. Thus,our final samples constitute 1,923 and 1,640 participantsfor the two studies (mean age 39 and 37 years, 58% and

60% females), respectively. Participants gave informedconsent (see OA Section F) and were reimbursed with$1.50. The median response time was 12 minutes withboth surveys.

Participants were asked to read a Facebook post onimmigration. Participants in Study 6 also saw a non-political post and selected the one that they would bemost likely to reply to in real life. Next, all participantsrated the hostility of the (selected) post on a scale from0 to 100. The consecutive page prompted participants inboth studies to write a comment to the post. To reducethe likelihood that participants simply parrot the target,we hid the target post from this screen. Finally, parti-cipants answered a question about their comprehen-sion of the task. Those who failed to select either of thetwo correct answers from the six available options wereexcluded from the study.

To judge the hostility of political posts and com-ments, we relied on crowd sourcing to rate all materialsand data involved in our experiments. Specifically, weinvitedMTurkworkers to rate the perceived hostility ofmessages (median response time was under five min-utes; raters were reimbursed with $0.60). Relying onuntrained raters to assess hostility means we could letregular people define what they find hostile, instead ofcoming up with a coding scheme that may or may notcapture peoples’ own experiences.10

Results

Offering participants an opportunity to choosebetween a political and a nonpolitical post in Study6 allows us to increase ecological validity by investigat-ing the behavior of respondents most likely to engagewith the given text in real life. In this study, we find thatpeople higher in status-driven risk taking are morelikely to pick the political post (β = 0.11, p = 0.08).Consistent with the results of Studies 1, 2, and 4, hostilepeople have an enhanced preference for participatingin political discussions.

Does our self-reported political hostility scale correl-ate with hostile behavior in Study 6? Yes. We validateour self-reportedmeasure of online hostility from Stud-ies 1–3 by observing a significant and substantial cor-relation (p < 0.001) between this measure and thehostility of the participants’ written responses, corres-ponding to about 30% of a standard deviation in the

9 These two experiments also allow us to test a number of additionalimplications relevant for our study of online political hostility—theseare reported in detail in OA Section G.

10 First, we relied on 1,430 raters to design the 16 versions of thetarget Facebook post, ensuring that they are reasonably evenly pacedalong the hostility scale. Each participant rated only one comment tomimic the design of the experiment and to avoid demand effects dueto seeing multiple versions of the stimulus. We ran bootstrappedsimulations on the target post ratings to estimate the ideal number ofraters per message for the participant comments. We found that theinterrater reliability across two independent sets of ratings reachesacceptable levels after five raters (Krippendorff’s alpha > 0.8), andadding extra raters after 10 ratings has quickly diminishing returns.Accordingly, to rate respondents’ responses from Studies 5 and 6, weinvited 787 and1,459participants, respectively, each rating 12 randomcomments (see more details in OA Section F). Workers were onlyeligible to participate in a single phase of the study as either partici-pants or raters.


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dependent variable (see online appendix G1). Thisassociation is all the more noteworthy, as we observedit relying on a single message in an artificial settingwithout real stakes. We know from Studies 1–3 thatpolitical hostility is a relatively rare phenomenon: eventhe more hostile respondents appear to keep their calmin most of their interactions. Consequently, it is notsurprising that we do not see huge differences betweenpeople at various levels of the scale.Do people on average write comments that are more

hostile than the original post despite instructions tomatch tone? No. In Study 7, we subtract the crowd-sourced mean hostility of the given target post from themean hostility of the reply to measure the averagedifference in hostility between the target post and thecomment written to it.11 Positive values indicate com-ments more hostile than the target, whereas negativevalues denote less hostile comments. Next, we performa simple one-sample t-test to investigate whether theaverage distance is statistically significant from 0. Wefind that comments are on average 11 percentagepoints less hostile than the target (ΔM = −0.11, t(1639) = −25.6, p < 0.001). This indicates that theaverage respondent is unlikely to suffer from a percep-tion bias. Still, about a quarter of our sample did writecomments more hostile than the target. Next, we inves-tigate whether individual differences correlate withcomment hostility in this task.Do people higher in status-driven risk taking write

more hostile comments despite instructions to matchtone? No. We regress the response hostility variableon SDRT allowing for varying intercepts for targethostility (8 levels) and target position (2 levels). Thereis no evidence that varying slopes for the personalityvariables improves model fit (p = 0.51 and p = 0.94,respectively), and they are omitted from the model. Inline with the instructions, more hostile target postsreceived more hostile responses. However, we do notfind that high status-drive respondents are more likelyto overreact in the task. In other words, while the initialvalidation tests showed a relationship betweenresponse hostility and status drive, the instruction tomatch the tone of the target Facebook post completelywashes away this relationship.

STUDY 8: TESTING THE CONNECTIVITYHYPOTHESIS

Studies 1–7 indicate that, for the most part, peoplebehave and process information in similar ways offlineand online. The question is, then, what the cause of thehostility gap is. We have raised an alternative to themismatch hypothesis, focusing not on the psychological

effects of online environments but on the actual affor-dances of online discussion environments. Notably,online discussion environments are highly connected,public, and permanent. Consequently, the actions ofjust a few hostile individuals will be significantly morevisible online than offline.

Predictions

A primary observable implication of the connectivityhypothesis is that people are exposed to particularlylarge numbers of hostile actions against third partiesonline (vs. offline), whereas the hostility gap againstfriends and the self is smaller. In other words, peopleare exposed to hostile interactions involving strangersonline that are hidden from view offline.


We collected data from the samples employed in Stud-ies 2 (Denmark, N = 1,041) and 3 (USA, N = 998) totest this notion. Specifically, we asked respondents howoften they witness attacks against self, friends, andstrangers. As before, we repeated these questions bothfor online and online discussions. We estimate theperceived frequency of witnessing an attack on(a) the target of attack (self, friends, or strangers),(b) the environment (offline or online), and (c) theirinteractions with OLS models. We report standarderrors clustered at the level of individuals.

Results

Respondents reported witnessing more hostilityagainst each of these parties online than offline. How-ever, as Figure 6 demonstrates, the gap between thetwo environments is largest for witnessing attacks onstrangers (USA: β online = 0.06, SE = 0.01, p < 0.001;Denmark: β = 0.21, SE = 0.01, p < 0.001) and muchsmaller for friends (USA: β online� friends=−0.03, SE= 0.01, p < 0.001; Denmark: β online� friends = −0.14,SE = 0.01, p < 0.001) and the self (USA: β online� self= −0.04, SE = 0.01, p < 0.001; Denmark: β online� self= −0.17, SE = 0.01, p < 0.001).

These additional analyses are consistent with theconnectivity hypothesis, which entails that the percep-tion that online discussions are more hostile than off-line discussions is simply because people witness amuch larger number of discussions online as theybrowse through their feeds on social media.12

Given this, they perceive—without any bias—amuchlarger number of encounters where the discussants arehostile with each other or at the expense of “absent”

11 Here, we rely on crowd-sourced hostility ratings from Study6 participants, who rated the target post before the matching taskwas introduced. We replicate these results with Study 7 participants’ratings in online appendix G. A slightly different scale was employedin the pretesting of the target posts, so the two ratings are not directlycomparable (but the results replicate if we rely on those ratings tocalculate the difference scores, nonetheless).

12 Our talking about politics variables from Studies 1–4 offers add-itional evidence that, indeed, these experiences come from passivelywitnessing hostility rather than participating in discussions that con-tain hostility (against the respondent or others). Even if we comparespecifically political discussions with partners who disagree with therespondent, we find that, if anything, people talk less about politicswith disagreeable partners online than offline (S1USΔM= −0.04, p <0.001; S2 DK ΔM = −0.05, p < 0.001; S4 US ΔM = 0.01, n.s.).


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third parties or groups. To put it bluntly, people mightalso be faced with significant offline hostility if theywere able to monitor all the private chats at parties,bars, and dinner tables about, for example, minoritygroups. Thus, despite common concerns about thenegative effects of online echo chambers, perceptionsof online hostility may be exacerbated by the publicityand fluidity of these discussion environments (Eadyet al. 2019; Gentzkow and Shapiro 2011; Settle 2018).

CONCLUSION AND GENERAL DISCUSSION

In this article, we documented that online politicaldiscussions seem more hostile than offline discussionsand investigated the reasons why such hostility gapexists. In particular, we provided a comprehensive testof the mismatch hypothesis, positing that the hostilitygap reflects psychological changes induced by mis-matches between the features of online environmentsand human psychology. Overall, however, we foundlittle evidence that mismatch-induced processes under-lie the hostility gap.We found that people are not morehostile online than offline, that hostile individuals donot preferentially select into online (vs. offline) polit-ical discussions, and that people do not overperceivehostility in online messages.We did find some evidencefor another selection effect: nonhostile individualsselect out from all, both hostile and nonhostile, onlinepolitical discussions. Thus, despite the use of studydesigns with high power, the present data do not sup-port the claim that online environments produce rad-ical psychological changes in people.Our ambition with the present endeavor was to

initiate research on online political hostility, as moreand more political interactions occur online. To thisend, we took a sweeping approach, built an overarchingframework for understanding online political hostility,and provided a range of initial tests. Our work high-lights important fruitful avenues for future research.First, future studies should assess whether mismatches

could propel hostility in specific environments, plat-forms, or situations, even if these mismatches do notgenerate hostility in all online environments. Second,all our studies were conducted online, so it is importantfor future research to assess the mismatch hypothesisby using behavioral data from offline discussions. Con-trasting online versus offline communications directlyin a laboratory setting could yield important newinsights on the similarities and differences betweenthese environments. Third, there is mounting evidencethat, at least in the USA, online discussions are some-times hijacked by provocateurs such as employees ofRussia’s infamous Internet Research Agency. Whilerecent research implies that the amount of contentgenerated by these actors is trivial compared with thevolume of socialmedia discussions (Bail et al. 2020), theactivities of such actors may nonetheless contribute toinstilling hostility online, even among people who arenot predisposed to be hostile offline.

Most importantly, however, our findings suggest thatfuture research could fruitfully invest in developing andtesting themain alternative to themismatch hypothesis,the connectivity hypothesis. Thus, our findings suggestthat the feeling that online interactions are much morehostile than offline interactions emerges because hos-tile individuals—especially those high in status-drivenrisk taking—have a significantly larger reach online;they canmore easily identify targets, and their behavioris more broadly visible.

We recommend future research that examines boththe technological and psychological sides of the con-nectivity hypothesis. In terms of technology, we do notknow which features are the most responsible for thisconnectivity: is it recommendation algorithms, the per-manence of online communication, the salience ofcross-cutting networks, or something else? In terms ofpsychology, further research is required on the motiv-ations of those who are hostile. Is it possible that manyhostile online messages are not meant to reach mem-bers of the out-group (e.g., an offensive joke meantonly for close, like-minded friends) or are hostile

FIGURE 6. Average Exposure to Attacks against Various Parties Online and Offline


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individuals directly motivated to engage their politicalopponents? Status-driven motives could underlie bothbehaviors, and more research is needed to understandtheir relative importance.Further research on the relative importance of situ-

ational and personality factors is also required. On onehand, most of our respondents are rarely hostile. Con-sistent with the suggestion of Cheng et al. (2017),situational triggers likely play a large role in determin-ing whether and when these respondents become furi-ous. On the other hand, the evidence against themismatch hypothesis implies that online environmentsdo not contain more anger-inducing situations. “Any-one can become a troll” online (as suggested by Chenget al. 2017), just as anyone can get angry offline, but asour findings demonstrate, those high in status-drivenrisk taking are much more likely to do so.13 Our resultsindicate that at least among these people, aggression isnot an accident triggered by unfortunate circumstancesbut a strategy they employ to get what they want,including a feeling of status and dominance in onlinenetworks.In summary, our research suggests that people do not

engage in online political hostility by accident. Onlinepolitical hostility reflects status-driven individuals’deliberate intentions to participate in political discus-sions and offend others in both online and offlinecontexts. In large online discussion networks, theactions of these individuals are highly visible, especiallycompared with more private offline settings. Theseresults imply that policies against hostility should seekto reduce the connectivity of hostile individuals, forexample, by decreasing the visibility of the content theyproduce or increasing the possibility of enforcing legalactions against illegal behavior (Haidt and Rose-Stock-well 2019). Of course, these policies need to be finelybalanced in order to avoid curtailing the freedom ofexpression. Furthermore, our findings show that normsof civility are somewhat weaker online than offline andcontinued exposure to hostile messages may increasethis gap, potentially propelling more hostility through avicious cycle (Brady et al. 2021). Consequently, ourresearch is consistent with prior findings that interven-tions strengthening norms or highlighting norm viola-tions can reduce hostility (Matias 2019; Siegel andBadaan 2020). At the same time, it is relevant tohighlight the danger of encouraging users to set andto police norms of civility, as this may ignite novelforms of hostility about appropriateness and couldundermine goals of improving the general tone ofonline discussions. From this perspective, public dis-course should inform the setting of norms, but third-party referees—such as platform policies or trainedmoderators—would be mainly responsible for promot-ing and enforcing them in specific discussion networks.

SUPPLEMENTARY MATERIALS

To view supplementary material for this article, pleasevisit http://dx.doi.org/10.1017/S0003055421000885.

DATA AVAILABILITY STATEMENT

Replication and reproductionmaterials can be found atthe American Political Science Review Dataverse:https://doi.org/10.7910/DVN/8I6NOT.

ACKNOWLEDGMENTS

This research has benefitted from discussions with VinArceneaux, Matt Levendusky, Mark Van Vugt, JohnTooby, and members of the Research on Online Polit-ical Hostility (ROHP) group, among many others. Weare grateful for constructive comments to workshopattendees at the Political Behavior Section of AarhusUniversity, at the NYU-SMAPP Lab, at the NYUSocial Justice Lab, at the Hertie School, and to confer-ence audiences at APSA 2019, HBES 2019, and ROPH2020.

FUNDING STATEMENT

This study was funded by grant AUFF-E-2016-9-22from the Aarhus University Research Foundationand grant CF18-1108 from the Carlsberg Foundation,both awarded to Michael Bang Petersen.

CONFLICT OF INTEREST

The authors declare no ethical issues or conflicts ofinterest in this research.

ETHICAL STANDARDS

The authors affirm that this article adheres to theAPSA’s Principles and Guidance on Human SubjectResearch.

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