How do climate change skeptics engage with opposing views? Understanding mechanisms of social identity and cognitive dissonance in an online forum
HHow do climate change skeptics engage withopposing views?
Understanding mechanisms of social identity and cognitive dissonance in an online forum P REPRINT
Lisa Oswald ∗ Hertie SchoolFriedrichstraße 18010117 Berlin, Germany [email protected]
Jonathan Bright
Oxford Internet InstituteUniversity of Oxford1 St Giles’, Oxford OX1 3JS, UK [email protected]
February 15, 2021 A BSTRACT
Does engagement with opposing views help break down ideological ‘echo chambers’; or does itbackfire and reinforce them? This question remains critical as academics, policymakers and activistsgrapple with the question of how to regulate political discussion on social media. In this study,we contribute to the debate by examining the impact of opposing views within a major climatechange skeptic online community on ‘Reddit’. A large sample of posts (N = 3000) was manuallycoded as either dissonant or consonant which allowed the automated classification of the full datasetof more than 50,000 posts, with codes inferred from linked websites. We find that ideologicallydissonant submissions act as a stimulant to activity in the community: they received more attention(comments) than consonant submissions, even though they received lower scores through up-votingand down-voting. Users who engaged with dissonant submissions were also more likely to returnto the forum. Consistent with identity theory, confrontation with opposing views triggered activityin the forum, particularly among users that are highly engaged with the community. In light of thefindings, theory of social identity and echo chambers is discussed and enhanced. K eywords Online community · climate change skepticism · reaction to opposition · dissonance · opinion polarisation · social identity · echo chamber Introduction
Scientists worldwide consider climate change as one of the greatest challenges of our time (IPCC, 2015). Its dynamics,consequences and roots in human activity attract enormous scientific consensus (Cook et al., 2016). However, despitethis consensus public skepticism about climate change remains significant in many countries (Poberezhskaya, 2018).For example, only 29% of U.S. conservatives attribute climate change to human activity (Funk & Rainie, 2015), whilstthe European Social Survey shows that climate change skepticism is expressed by 3 to 10% of respondents in WesternEurope. In Eastern European countries such as the Czech Republic, Lithuania and Estonia, as well as in Israel and inRussia, such skepticism is present in the answers of 10-15% of respondents (Poortinga, Whitmarsh, Steg, Böhm, &Fisher, 2019). Findings from a representative UK study (Poortinga, Spence, Whitmarsh, Capstick, & Pidgeon, 2011)and the Eurobarometer (McCright, Dunlap, & Marquart-Pyatt, 2016) reinforce these results. Such skepticism inevitablyhas the potential to impede both action on climate change in particular (Runciman, 2017) and also trust in science moregenerally. (Almassi, 2012; Beck, 2012; Hmielowski, Feldman, Myers, Leiserowitz, & Maibach, 2014). ∗ OrcidID: https://orcid.org/0000-0002-8418-282X, Twitter: @LisaFOswaldo a r X i v : . [ c s . S I] F e b swald & Bright - PREPRINT - February 15, 2021While climate change skepticism has a long history, it currently often manifests itself in debates and discussions onsocial media (see, for example, studies by Pearce, Niederer, Özkula, and Sánchez Querubín 2019, Williams, McMurray,Kurz, and Lambert 2015, Tyagi, Babcock, Carley, and Sicker 2020, Samantray and Pin 2019 and Treen, Williams, andO’Neill 2020). Climate change skeptics have the opportunity to gather and organise in online forums, emphasisinguncertainties about scientific findings, spreading contrary opinions and weakening the support for political actionon mitigation and adaptation strategies. This largely informal and non-institutional communication often excludesrobust scientific information (Schäfer, 2012). Instead, those skeptical about climate change can establish a supportive,networked space linked to other climate change skeptical sites; one that distances readers from original sources ofscientific information. Within this network, users engage in a variety of rhetorical strategies to echo skeptical opinionsand to discredit opposing views (Bloomfield & Tillery, 2019). In such spaces, climate skepticism acts as a local majority:and such ‘perceived consensus’ has strong potential to affect opinion formation (Lewandowsky, Cook, Fay, & Gignac,2019; Marks & Miller, 1987).While many researchers in the field of such climate change ‘echo chambers’ (Sunstein, 2002) identify patterns ofpolarization but refrain from giving clear recommendations on how to prevent or mitigate them (Brüggemann, Elgesem,Bienzeisler, Gertz, & Walter, 2020; Elgesem, Steskal, & Diakopoulos, 2015; Kaiser & Puschmann, 2017), currentwork has started to propose interventions that promote engagement with cross cutting views and opinions as a way ofundermining this type of ideological enclave (Edwards, 2013; Nordbrandt, 2020; van Eck, Mulder, & van der Linden,2020; Walter, Brüggemann, & Engesser, 2018-02; Williams et al., 2015). However, more recent research in othercontexts has suggested that such exposure may backfire (Bail et al., 2018; Paluck, 2010). Furthermore, experimentalevidence shows that skepticism is a complex issue which is hard to address or ‘debunk’ (Cook et al., 2013-05).Communication attempts that focus on the scientific consensus (T. A. Myers, Maibach, Peters, & Leiserowitz, 2015;Van der Linden, Leiserowitz, Feinberg, & Maibach, 2014), or that use targeted messages that resonate with audiencevalues such as religion or a free market ideology (Campbell & Kay, 2014; Dixon, Hmielowski, & Ma, 2017) offer somepotential. However, Dixon et al. (2017) did not find significant effects for scientific consensus messaging, and overall,backfire or boomerang effects (Lodge & Taber, 2013) can increase climate change skepticism within these attempts(Cook et al., 2016; P. S. Hart & Nisbet, 2012; Zhou, 2016). Hence, overall the effect of cross cutting and opposinginformation on climate change skeptic views remains highly unclear.In this study, we build on this previous literature with an observational study of how climate change skeptics reactengage with opposing viewpoints. Our work is based on theories of social psychology and social cognition, which offerdiverging expectations about the consequences of dissonant material when it enters an echo chamber. The empiricalpart of our work looks at r/climateskeptics, a part of Reddit that is dedicated to the critique of the concept of climatechange. Our results show the nuanced ways in which individuals in an echo chamber are able to manage and cope withthe dissonance they encounter towards their views. Theorising responses to opposition in climate change skeptic communities
We ground our study in social identity theory. This theory deals with the portion of an individual’s self-concept thatis derived from perceived membership of a social group (Tajfel, Turner, Austin, & Worchel, 1979). It argues thatinter-group contact, in contrast to the literature on within group polarisation (Cialdini & Goldstein, 2004; Jones &Roelofsma, 2000; D. G. Myers & Lamm, 1976; Yardi & Boyd, 2010), has the potential to intensify the need to maintainone’s identity by identifying with the ingroup and arguing against the outgroup (Hogg, Sherman, Dierselhuis, Maitner,& Moffitt, 2007; Tajfel et al., 1979; Turner & Pratkanis, 1998). The way individuals categorise the world into mentalschemes is the core principle of social cognition (Fiske & Taylor, 1991) that can lead to ingroup preference, stereotypesand logic errors (Aronson, Wilson, & Akert, 2014).Following the suggestion of Jost and Krochik (2014), who actively call for the incorporation of theories of motivatedsocial cognition into studies of information-exposure, in this paper we test two lines of thinking about the nature ofresponses to opposing viewpoints within a climate change skeptic community: mechanisms of selective exposure andmechanisms of social identity and motivated social cognition. Such theories, it is worth highlighting, have been widelyapplied in previous studies of climate change skeptic communities (Corner, Whitmarsh, & Xenias, 2012; Douglas,Sutton, & Cichocka, 2017; Häkkinen & Akrami, 2014), which have often connected such skepticism to issues of identityrather than simple lack of understanding or information (Hogg, 2014; Hogg & Adelman, 2013). Such works build onthe observation that there are often gaps between environmental attitudes and behaviour (Bamberg & Möser, 2007;Kollmuss & Agyeman, 2002), and gaps between information and attitude (Arcury, 1990; Bradley, Waliczek, & Zajicek,1999) in the context of climate change.The empirical component of our paper is based on the social news aggregation, content rating and discussion platform‘Reddit’. On Reddit, users are registered under a pseudonym. They are able to create ‘submissions’ in a large number2swald & Bright - PREPRINT - February 15, 2021of ‘subreddits’, which are parts of the site dedicated to the discussion of specific topics. These submissions are oftenlinks to other websites (though can also be text, images, or videos). Other users can then read, up-vote, down-vote,and comment on the submission. Reddit is a highly frequented website and most content is publicly available whichmakes it an interesting resource of organic content for social scientists (Amaya, Bach, Keusch, & Kreuter, 2019). It isalso a good case for our study because it contains a large climate change skeptic subreddit: r/climateskeptics. Thissubreddit is the focus of our study. It represents, arguably, an echo chamber (Sunstein, 2002): a place where a radical,counter-cultural worldview which is a minority view in society at large can nevertheless thrive and be in a local majority(Bright, Marchal, Ganesh, & Rudinac, 2020). In what follows below, we elaborate theoretical expectations for the typesof reactions dissonant viewpoints might provoke in the context of such a space.We will begin by looking at the type of direct responses that dissonant submissions might provoke. In line with theoriesof social cognition, it is likely that members of a community rate opinions and information provided by members oftheir ingroup higher than content coming from an outgroup. This is due to mechanisms of ingroup favoritism andnegative outgroup stereotypes (Zebrowitz, Bronstad, & Lee, 2007), as well as mechanisms of motivated social cognition(Jost & Amodio, 2012). This is also supported by the theory of cognitive dissonance (Festinger, 1957; Greenwald &Ronis, 1978) which states that information or media messages that challenge people’s beliefs typically create a feelingof dissonance, which is unpleasant and something most people avoid (W. Hart et al., 2009). Cognitive dissonance canbe reduced by (amongst other methods) reducing the importance of dissonant cognitions, or increasing the importanceof consonant cognitions (Harmon-Jones & Mills, 2019).In our particular case, the critical method for indicating the quality of a submission in Reddit is voting: users can‘upvote’ submissions they agree with or think are important, and ‘downvote’ others. Upvotes and downvotes arecombined together to form a score for each individual submission, which is a critical part of how Reddit organisesthe presentation of content to users. Hence, building on the theories outlined above, we expect that the importanceof a dissonant submission could be lowered by undermining its legitimacy through ‘downvoting’ it. Conversely, suchopposing submissions can be further undermined by ‘upvoting’ consonant ones. We hence develop our first hypothesis: Hypothesis 1:
Submissions featuring opposing views and dissonant information will receive lower scores thanconsonant ones.The theory of motivated reasoning (Jost, Glaser, Kruglanski, & Sulloway, 2003) assumes that attitude strength isan important moderator for cognitive biases. Karlsen, Steen-Johnsen, Wollebæk, and Enjolras (2017) found that tosome extent, people seek supportive messages, but that it is more difficult to show that people avoid contradictoryinformation (Brundidge, 2010). The theory further assumes that prior attitudes bias how people process arguments, andthat this bias is reinforced not only through selective exposure, but also through selective judgement processes (Lebo &Cassino, 2007; Taber, Cann, & Kucsova, 2009; Taber & Lodge, 2006). The first mechanism, the confirmation bias orattitude congruency bias (Taber et al., 2009), assumes that people tend to evaluate arguments that support their views asstrong and compelling, which would be in line with the argument that echo chambers lead to polarised opinions. Thesecond mechanism is disconfirmation bias, according to which people use time and cognitive resources to degrade andcounter argue opposing views (Taber et al., 2009). Therefore, when presented with contrasting arguments in onlinedebates, these arguments may lead to even stronger beliefs in initial opinions. For example, Karlsen et al. (2017) foundthat people engaged actively with dissonant content from the outgroup and that their opinions were reinforced bycontradiction as well as confirmation.In our particular context, apart from simply voting, contradiction can also be signalled by commenting on a submission.Of course, comments can be positive or negative, however we expect that dissonant posts provide a stronger motivationto respond than consonant ones, based on the reasoning above (it is worth noting that our twin hypotheses of lower scoreand higher comments goes against the general logic of Reddit, in which score and comments are typically positivelycorrelated - see Singer, Ferrara, Kooti, Strohmaier, and Lerman (2016)). Therefore, we develop our second hypothesis:
Hypothesis 2:
Submissions featuring opposing views and dissonant information will trigger more activity (comments)than climate change skeptic ones.The temporal Need-Threat Model, originally developed in the context of social ostracism (Williams, 2009), assumesthat when fundamental needs (such as the needs for belonging, self-esteem, meaning, and control) are threatened,people initially react in a quick and reflexive way that includes strategies of need fortification. Needs for belongingand self-esteem can be fortified through attempts to become more socially attractive. This can be achieved by beingattentive to social cues, as well as overall compliance and conformity. A threatened need for control and recognition The sum of positive up-votes and negative down-votes results in the ‘score’ of a submission.
Hypothesis 3:
Submissions featuring opposing views and dissonant information are more likely to attract commentsexpressing negative sentiment.However, if these attempts to undermine or contradict dissonant information are unsuccessful people can reach aresignation stage (Williams, 2009). With increasing frustration through ongoing inability to fortify needs of controland self-esteem, social withdrawal can occur. Another possible coping mechanism can be engagement with other,more pleasant activities. This form of distraction is conceptualized as an accommodative, secondary control copingstrategy (Allen & Leary, 2010). In the case of online communities, we might imagine that, following engagement witha dissonant submission, people would possibly suspend their engagement with a forum and move on to something else,which would again align with the tendency to avoid information that induces cognitive dissonance (Case, Andrews,Johnson, & Allard, 2005). In contrast to hypotheses 1 to 3, this effect is in line with the mechanisms of selectiveexposure in online echo chambers, where people are predominantly seek exposure to attitude, control and self-esteemreinforcing information.
Hypothesis 4:
Engagement with opposing views and dissonant information makes users more likely to leave theforum.It is also worth considering the type of user that responds to dissonant content. It is by now well known that thedistribution of activity in online forums is skewed: a highly active minority typically accounts for a significant percentageof all the activity taking place (Bright, Bermudez, Pilet, & Soubiran, 2019). In the work of Barberá, Jost, Nagler,Tucker, and Bonneau (2015), these levels of contribution activity was also related to political extremism, suggesting thatthese highly active individuals also hold stronger views. The majority of content was created by a minority of extremeconservatives and extreme liberals; and the relationship between extremism and the formation of echo chambers hasalso been documented in other work (Bright, 2018).According to Social Identity Theory (Tajfel et al., 1979), the structure of an individual’s social identity depends onthe relevance of perceived membership of a social group or community. Often, an individual is a member of varioussocial groups, such as a family, a work team, sports teams, etc. In the digital sphere, this spectrum expands to onlinecommunities. Furthermore, the more strongly people engage with a community, the more relevant is this membershipfor their social identity. In case identification with one particular social group dominates the social identity concept ofan individual, a threat to the identification with this group can pose substantial stress on the individual (Haslam et al.,2008; Praharso, Tear, & Cruwys, 2017). In the case of power users, who spend a considerable amount of time engagingwith an online community, their social identity may be more strongly affected by external threats to or dissonanceswithin the online community, compared to users whose social identity depends less on their identification with theonline community due to a lower level of engagement. In line with the Need-Threat Model, this identity threat, orin other words, the threatened need to belong, can be dealt with in various forms, such as the active confrontation orcounter argument against users who threaten the consistency of community beliefs when injecting opposing views anddissonant information into the community.
Hypothesis 5:
More senior users, those with higher levels of past community engagement, are more likely to engagein discussions in reaction to submissions featuring opposition to climate change skepticism, compared to users with alower level of past engagement with the community.
Methods
Data Collection
Data for the study was collected from the r/climateskeptics subreddit . This subreddit was created in July 2008 andencourages skepticism and debate about the concept of climate change. It contains more than 50,000 submissions onthe topic, the majority of which (92%) are simply links to websites, blogs, articles or videos. These submissions have, See:
Measures
The most important measure in our study concerns whether a submission to r/climateskeptics should be labelled‘consonant’ or ‘dissonant’. A consonant submission, in this context, would be one that supports the majority opinion onthe forum by putting forward views that are skeptical of climate change. A dissonant submission, by contrast, wouldbe one that supports the scientific consensus on climate change (Cook et al., 2016) and attacks or undermines climatechange skeptics. This dichotomy is of course a simplification of the range of views someone can have on climatechange: for example many of the people in r/climateskeptics were simply skeptical about some of the aspects of climatescience such as the anthropogenic cause or the negative consequences for humans rather than denying outright thewarming trend in the earth’s climate (see Leiserowitz et al. (2020) for an overview of different styles of reasoning onclimate change). Nevertheless, this simplification captures the essence of what we want to study.We approached this labelling task in two ways. First, we took a randomly drawn sample of 3,000 submissions, and oneauthor of the study manually coded each one as either ‘consonant’ or ‘dissonant’. Such a manual approach seemedimportant because it allows for the interpretation of ironic or sarcastic content, image material or the disapprovingreference of climate activist content, which would be mislabelled using an automated or purely semantic approach(Grimmer & Stewart, 2013; Metag, 2016; Riffe, Lacy, Fico, & Watson, 2019). A second author double coded a randomlydrawn sample of 100 of these submissions, to calculate an estimate for the inter-rater reliability of the coding. Overallthere was an 86% agreement between coders, with a Krippendorff’s alpha of 0.62, indicating a substantial accordance(McHugh, 2012).Second, we leveraged the manual coding to assign an automatic dissonance code to every submission in the dataset.We did this by exploiting the fact that 92% of the submissions in our dataset were simply links to external sites, asdescribed above. We used the data from our manually coded sample to infer a code for these external sites, by taking anaverage of codes from submissions that contained this link in our manually coded sample. These inferred codes canthen be applied to other submissions in the dataset. For example, the Daily Caller appeared 46 times in our manuallycoded data. Two of these submissions were coded as dissonant, and 44 of them were coded as consonant. Therefore,in our automatically coded data, all submissions containing the Daily Caller received an automatic dissonance codeof = 0 . . One might question how some sites can be used to advance both consonant and dissonant positions.However, one thing we remarked on in the coding was how easy it was to use a link from an outlet to support whateverposition the author of the submission wanted. For example, the Guardian is a site that clearly supports the idea thatclimate change is a substantial threat (Carrington, 2019). It featured 40 times in our data, however the outlet received anaverage dissonance code of 0.175 in our data, which means it was largely used to support climate change skepticalpositions.Using these methods, we were able to infer an automatic dissonance code for 83% of the submissions in our dataset.The remaining 17% of submissions could not be assigned a code in this way, either because they did not contain a link,or because they contained a link which was not contained in our manually coded data. These submissions were given5swald & Bright - PREPRINT - February 15, 2021Table 1: Descriptive statistics Manual labelling Automatic labellingDissonance (mean) 0.094 0.095Dissonance (sd) 0.292 0.151Score (mean) 14.21 13.73Score (sd) 31.79 30.31Num. comments (mean) 6.88 7.02Num. comments (sd) 14.65 14.74Comment sentiment (mean) 0.49 0.54Comment sentiment (sd) 1.47 1.51% Text only 92% 92%% Deleted 92% 92%Num.Obs. 3,000 50,502
Note. ‘Dissonance’ in relation to climate change skepticism; ‘score’ assum of upvotes and downvotes; ‘text only’ representing submissionswithout links to external websites; ‘deleted’ representing submissionswhich were subsequently removed. dissonance codes based on the average dissonance code of other submissions the author had made, on the basis thatauthor were likely to be relatively consistent in their beliefs. If even that was not possible (if the author themselves hadno other coded submissions), then the submission was given a code based on the average dissonance code of all otherauthors (see Appendix A for more details). As part of our diagnostic checks, we checked the sensitivity of our results tothis method of imputation, as described in Appendix B. We found no evidence that the imputation substantially affectedthe results.We also coded the sentiment present in the comments in our dataset. We made use of a widely used approach, whichdetermines the sentiment of a text section as the sum of the sentiment content of the individual words (Silge & Robinson,2016). Out of various general-purpose dictionaries that encode sentiment for single words, we chose the NRC sentimentdictionary (Mohammad & Turney, 2013). This dictionary, constructed using crowdsourcing, categorises words aspositive or negative. The NRC sentiment dictionary was validated on online communication data such as restaurant ormovie reviews (Kiritchenko, Zhu, Cherry, & Mohammad, 2014) and conversations on Twitter (Mohammad, Kiritchenko,& Zhu, 2013) which is relatively close to the type of content we face in our project. Once we had applied the NRCdictionary to the text of the comments, the resulting sentiment for each comment was defined simply as the number ofpositive words minus the number of negative words. Of course, it is worth noting that sarcasm or negated text can be achallenge for the validity of automated sentiment analysis (Silge & Robinson, 2016).In addition to these coding exercises we also collected a number of other variables which were relevant for the study.We recorded the ‘score’ of each submission. This score is simply the number of up-votes minus the down-votes asubmission receives. With this metric, absolute votes are concealed to mitigate the influence of spam bots (RedditInc, 2021b). We recorded the number of comments a submission received, and the overall number of contributions(submissions and comments) by each user in the dataset. Finally, we recorded the timestamp of the submission and thenumber of comments in reaction to the submission. All of these pieces of data were collected from the PRAW API.Descriptive statistics for the measures are shown in Table 1. The average dissonance code is low, as we would expectin a forum which is largely dedicated to the idea of climate change skepticism. The dissonance codes are also quiteconsistent between the manual and automatic labelling. The metrics for score, the number of comments and sentimentall show skewed distributions, which suggests they should be log transformed before analysis. Analysis
In Table 2 we present our first set of results, examining the reactions generated by dissonant content within the r/skepticssubreddit. Each model in Table 2 is a fixed effects linear regression, and each observation in the model is a differentsubmission made to the forum. We include fixed effects on the year, day of week and hour of posting to control fortemporal effects in the data, as both submission and comment levels vary strongly over time. We also include whetherthe post is a ‘text only’ post (and contains no link within it) and whether the post has been deleted as control variables. For example, a score of 2 could mean that a submission received 3 up-votes and 1 down-vote, or that it received 50 up-votes and48 down-votes.
Score Comments SentimentM1.1 M1.2 M2.1 M2.2 M3.1 M3.2(Manual) (Auto) (Manual) (Auto) (Manual) (Auto)Dissonance 0.32*** 0.29*** 2.00*** 1.77*** 1.00 1.00Text Only 0.61*** 0.70*** 1.80*** 1.88*** 1.01 1.01***Deleted 0.55*** 0.54*** 0.62*** 0.62*** 1.00 1.00Num.Obs. 3,000 50,502 3,000 50,502 2,140 35,730Adj. R (full) 0.277 0.203 0.139 0.119 -0.001 0.004Adj. R (proj.) 0.143 0.080 0.056 0.044 -0.020 -0.000* p < < < We use HC1 robust standard errors for all models (Long & Ervin, 2000). Standard diagnostic checks were appliedwhich provided no reason to doubt the results. A full record of diagnostics can be found in Appendix B. Each outcomevariable is log transformed. All coefficients are exponentiated, and hence can be interpreted as percentage changes.We will begin with hypothesis 1, which suggested that dissonant submissions ought to receive a lower score thanconsonant ones. Models 1.1 and 1.2 test this proposition using our manually and automatically coded data. In both ourmanual and automatic models, dissonant submissions received around 70% lower scores than submissions confirmingclimate change skepticism, hence providing strong supporting evidence for hypothesis 1.Hypothesis 2 suggested that dissonant submissions ought to receive more comments. Again we find support for thisidea in both our manual and automatic models: submissions undermining climate change skepticism received 100%more comments than those which do not in our manually coded model, and 77% more in the automatically coded model.Hence hypothesis 2 is also supported.Hypothesis 3 suggested that comments on dissonant submissions would have a more negative sentiment than commentson submissions supporting climate change skepticism. Here we find no evidence of any relationship in either ourmanual or automatic models, and an extremely low R value for both of them. So hypothesis 3 is not supported. It isworth noting that M3.1 and M3.2 can only be estimated for submissions that attracted at least one comment. Hence, theN is lower than in models M1.1 - M2.2.One point worth noting across all models is the the consistency of results between our manual and automatic approaches.This suggests that our automatic approach offers a good proxy for the more reliable but smaller scale manual coding.Our next question concerned what happens after people have engaged with dissonant material, which we define hereas having commented on a dissonant submission. We hypothesised (H4) that those having their beliefs challenged inthis way would be less likely to return to the forum than those who had engaged with attitude confirming information.We address this question using a PWP-Gap Time model, a type of event history analysis (Amorim & Cai, 2015). Thismodel (M4.1) is presented in Table 3. Each observation in the model is a comment to the subreddit made by one of theauthors in the dataset on one of the submissions. By looking at the characteristics of the submission which attractedthe comment, and the time elapsed until the next comment, we can analyse how engagement with either consonant ordissonant submissions relates to the probability of returning to the forum and commenting again.We only present a version of this model using automatically coded data, as it presupposes complete comment historiesfor each author, which of course cannot be achieved with the manual data. The model clusters standard errors at theauthor level, takes into account the number of previous comments made by each author, and also stratifies accordingto the hour, weekday and year of contribution. The number of comments made on the submission, and the averagesentiment of the comments, are included as control variables. Diagnostic checks for this model can be found inAppendix A2: they also provide no reason to doubt the results.The results of the model show that our hypothesis is reversed: those engaging with dissonant material were around 21%more likely to post again compared to those who did not. Having commented on submissions which attracted morecomments or which had more positive sentiment also increased the likelihood of coming back. A proportional hazardstest for the model shows that the effect varies over time. We show this variation in Figure 1. The horizontal line in thefigure indicates the average effect reported in M4.1. We can see that the effect of having engaged with a dissonant poststimulates a much stronger likelihood of coming back in the first hour or so (starting from around 50% more in thefirst minute). This then declines progressively over time. It is also worth commenting on the very low Cox-Snell R R (full) 0.097 0.095Adj. R (proj.) 0.021 0.033Cox-Snell R < < < Discussion
This study analysed the consequences of opposing views and dissonant information within a major climate changeskeptic online community. A number of findings are worth emphasising. First, we showed that dissonant submissions tothe forum attracted a lower score but a higher amount of comments. This finding, while expected from the literature weelaborate above, is an interesting reversal of general trends on Reddit where score and comments tend to be correlated(Singer et al., 2016). One thing this shows is how engagement in online discussion forums provides sophisticated toolsfor managing dissonance: individuals in the forum can at once undermine the credibility of the author (by down-votingit) but also engage strongly with it (by commenting). In a way, dissonance can be micro-managed in such settings,which (we speculate) may make minimising it easier.Furthermore, there is a tendency for more senior users to be especially engaged within the discussions in reaction tosubmissions that contain opposing views and dissonant information. This tendency hints towards the hypothesisedeffect of identity defence. Users highly engaged with r/climateskeptics might derive a greater part of their social identityfrom the identification with this social group. Consequentially, their experienced identity threat when confronted withopposing views leads to strategies of identity and community defence, in the form of argument countering engagementin the comments below an opposing post. Given that identity strength is an important moderator for motivated reasoning(Jost et al., 2003), our results align with the theory: those highly engaged with the forum are more likely to engage indiscussions triggered by opposing views. This is also in line with the findings suggesting that contribution activity isrelated to political extremism Barberá et al. (2015). However, the study of Barberá et al. (2015) did not differentiatebetween activity levels after engagement with dissonant and congruent information.Against our expectation of the long term effects of need threats, users who engaged with opposing views were morelikely to return to the forum than those engaging with attitude confirming skeptic content. However, given that theresignation stage proposed by the Need-Threat model might not be reached, this finding actually aligns with theexpectations from social identity theory. Identity threats produced by confrontation with opposing views may in factincrease the tendency to defend one’s identity and to reduce feelings of dissonance by returning to the forum and postingidentity reinforcing content into the subreddit. The fact that dissonant viewpoints are fundamental for stimulatingactivity in the forum and encouraging people to return may prompt a rethink of the overall idea of an echo chamber(which suggests that opposing voices must be entirely removed and marginalised).Even though active climate change skeptic community members appear to have a clear stance on the issue, commentingand countering dissonant posts from outgroup members who accept the scientific consensus, we do not find evidencefor more negative sentiment in these discussion threads. This is in line with qualitative findings that climate changeskeptics appear to aim for an argument-based discourse culture in the forum, rather than one focused on ad-hominemattacks. An interesting and closely related characteristic of the discourse in r/climateskeptics, which we remarked uponduring the coding procedure, is the emphasis on climate knowledge and overall competencies in STEM subjects amongits members (e.g. ‘It’s easy to claim a skeptic is a denier, if one doesn’t even know the names of the most fundamentaltemperature sets.’).Our study also contributes to the literature on modern climate change skepticism (Brüggemann et al., 2020; Edwards,2013; Elgesem et al., 2015; Kaiser & Puschmann, 2017; Lewandowsky et al., 2019; Lewandowsky, Oberauer, & Gignac,2013; van Eck et al., 2020; Walter et al., 2018-02; Williams et al., 2015) and mechanisms of online polarization (Bailet al., 2018; Nordbrandt, 2020; Sunstein, 2002, 2018). Most previous studies focus on the detection and descriptionof polarized networks. In contrast, we examine concrete responses to opposing views, a theoretically implied butbarely studied mechanism. While some studies view active engagement with opposing views as part of the solution toclimate change skepticism, we find empirical evidence that it may in fact be part of the problem, at least in our context.Methodologically, while most previous studies either focused exclusively on the issue of climate change in online blogs(e.g. Matthews 2015), established media outlets (e.g. Walter et al. 2018-02) or the social media context (e.g. Williamset al. 2015), we use a methodological approach that draws upon insights from various online arenas. For example, thein-depth examination of submissions with manual content coding allowed us to understand how publications like the
Guardian can be used to support climate change skeptic positions, by presenting isolated facts without context andreframing them to support climate change skepticism or by using them in a sarcastic way to mock the ‘alarmism’ of theoutlet. One can only speculate whether climate change skeptics generally perceive themselves as minority opinion.However, findings on other minorities suggest that they experience higher epistemic needs to maintain their identitywith intergroup discrimination than majority groups (Leonardelli & Brewer, 2001; Mullen, Brown, & Smith, 1992),which is in line with our findings of identity defence patterns in the community.9swald & Bright - PREPRINT - February 15, 2021
Limitations
While we consider Reddit a good platform for our study, it is worth noting a key limitation of our focus here, which isthat the sociodemographic characteristics of Reddit users are not comparable to the general population. During theregistration process, no personal information is collected and the demographics of the 330 million active users remainlargely unknown. However empirical investigations suggest that users are more likely to be male and younger than thegeneral population (Duggan & Smith, 2013; Singer, Flöck, Meinhart, Zeitfogel, & Strohmaier, 2014).Another limitation was our reliance on manual coding. Nevertheless, given the highly context dependent and oftenironic or sarcastic nature of submissions within r/climateskeptics, similar to the highly quantitative study of Williams etal. (2015), a manual approach was chosen over automated content coding using NLP or machine learning techniques(Kirilenko & Stepchenkova, 2012; Metag, 2016; Weitzel, Prati, & Aguiar, 2016).
Conclusion
A clear and possibly the most important finding of this study is, that in contrast to the classical theory of echo chambers,‘breaking up the echo chamber’ with information on the consequences of climate change does not seem to work. Whilethe major climate change skeptic community on Reddit, r/climateskeptics, is characterised by long-term stability, ourstudy uncovered mechanisms of identity defence in reaction to opposing views and dissonant information. Therefore,we emphasise the need for the reconsideration of policy implications of promoting access to more cross cuttinginformation. This line of reasoning proposes almost the opposite to the classical strategy of confronting problematiconline communities with opposing information. However, further work is required to suggest more about what effectivestrategies are in such a context.Issues of conspiracy theory, involving lack of trust in scientists and politicians, have gained more importance whenfacing public reactions towards policies addressing the Covid-19 pandemic across various countries (Constantinou,Kagialis, & Karekla, 2020). When the consequences of climate change become equally pressing and visible, and moreambitious mitigation policies are implemented, the ground may be prepared for climate change skeptic conspiracynarratives to push into the mainstream with potentially catastrophic effects for effective policy making. It is thereforecritical to understand and monitor climate change skepticism online in order to both gain insights about the discourseon climate change across the whole spectrum of the public sphere and to enable policy makers to act effectively whendynamics change.
Lisa Oswald is pursuing a PhD in Governance at the Hertie School in Berlin. She graduated from the University ofOxford, UK, with a MSc degree in Social Data Science, and from the University of Kassel, Germany, with a BSc andMSc degree in Psychology. She is interested in the public perception of climate change, political opinion formation,online communication dynamics and the emergence of collective behaviour.
Jonathan Bright is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute who specialisesin computational approaches to the social and political sciences. He has two major research interests: exploring theways in which new digital technologies are changing political participation; and investigating how new forms of datacan enable local and national governments to make better decisions.
Conflict of interest: none.
Supplementary Material: available upon request. After publication available in data repository of respective journal.
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As described in the main body text we imputed values for submissions that could not be automatically labelled usingour link based method. This occurred when the submissions did not contain a link or when the link was not containedin the coding dataset. In this case, the submission was given a value equal to the mean of all other submissions made bythe author. In cases where the author themselves had no previously coded submissions, then these submissions weregiven a mean of the average submission value of all other authors.Of the 50,502 submissions, 8,645 (17%) were imputed. 6,108 of these were imputed using the average of the author’ssubmission history, and the remaining 2,537 were imputed using the average of all authors.All of the models reported in the text were also estimated on a subset of data without the imputed data points. Theresults were substantially the same: statistical significance and direction of effect was not altered, and size of effect wasonly altered fractionally.
B Regression Diagnostics