Influence of social media comments on opinions about news

Influence of social media comments on opinions about news | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Influence of social media comments on opinions about news Federica Nisini, Jan Felix Weis, Silke Lux, Daniel Sensen, Wouter van den Bos, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7876614/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In the current age many people encounter news through social media. This mode of information acquisition is characterised by the presence of reader’s comments published together with the news. Previous studies have shown that these comments influence people’s opinion about the news, which raises questions about the vulnerability of public opinions. We developed an ecological task to replicate and investigate this phenomenon experimentally. In our task, participants were presented with headlines on important contemporary issues actually posted on Facebook, together with actual readers’ comments, in a display replicating the social media platform. Participants in two pre-registered studies run in different countries (USA, N = 220; Germany, N = 220) consistently adjusted their opinions in accordance with the sentiment expressed in the comments. The degree of opinion change was significantly greater when participants had weak pre-existing attitudes toward the topics and when they had low confidence in their initial opinions about the news. An exploratory analysis revealed greater susceptibility to social influence in less digitally mature participants. This study replicates under controlled conditions the powerful influence of online social media comments in shaping public opinion and points to some variables moderating this influence. Humanities/Cultural and media studies Social science/Cultural and media studies Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The Internet radically transformed how we interact and communicate with each other 1 . As of July 2025, about 5.76 billion people (70.0% of the global population) are active mobile internet users, and most spend time on social media sites 2 . These developments have resulted in online newspapers on social media becoming the main source of daily news 3 , with more news being consumed via online media than all other sources in the USA for example 4 . Characteristic of online news is the audience’s ability to comment on the content, usually immediately below the post, which fosters interactivity and discussion among readers 5 . Reading news thus becomes a collective activity 6 with the expression of a broader range of opinions and exponentially growing debate. Popular on social media sites are “snack news”, a format requiring low cognitive engagement and consisting of a news headline, a picture and a short preview of the news article 7 . While snack news are designed to only convey a general overview of a topic without gaining in-depth knowledge of it, Bakshy and colleagues found that just 7% of 10.1 million Facebook users clicked on the links to full articles about political and world affairs in their news feed 8 . As a result, readers feel informed about an issue without actual knowledge gain and might express stronger convictions on these topics than fully informed readers 9 . Since the rise of online news consumption, disinformation and misinformation have become a crucial contemporary problem, and were ranked in a 2025 survey of 1500 experts by the World Economic Forum as the top global risk during the next 2 years 10 . This is particularly concerning when these techniques are used to undermine established scientific knowledge, as observed in current debates on epidemics, vaccines or climate change 11 , 12 . A recent meta-analysis of over 300 studies of over 190’000 participants reports that participants seem to be overall skeptical of the veracity of online news and can detect false news when prompted to do so, but skepticism increases for news that are discordant with a reader’s political views, and skepticism may also lead a reader to not believe reliable information enough 13 . A recent review concludes that cognitive ability, thinking styles, and metacognitive scrutiny protect from misinformation, while early adverse experiences, ideology, and the removal of fact-checking on social media platforms facilitate it 14 . Snack news lend themselves particularly well to disinformation and misinformation campaigns. Clickbait headlines with emotional language and dramatic images draw attention and strong reactions such as outrage and indignation, which increase engagement metrics that speed up content spreading, including fake content, at the detriment of the moderate and balanced perspectives of professionally curated content 15 – 17 . Such campaigns were shown to have real-world impact on the 2016 US presidential election and the UK Brexit referendum for example 18 . Similarly, Facebook and YouTube contributed to the rise and unification of far right-wing parties in US and Germany through recommendation of polarising and conspiracist material 19 , 20 . Social cues including user-generated comments probably play a large role in the spread of dis- and misinformation 21 . Indeed, laypeople without proper expertise or reputation can express their opinions, emotions and reactions on social media and potentially reach as many users as respectable news sources 22 . Additionally, shallow and disrespectful comments can have negative repercussions on the material they are attached to, including on the perceived quality of professional and scientific articles 23 – 25 . Critical comments are powerful: they can be more effective at flagging fake-news than official disclaimers attached to the post made by the platforms themselves 26 , but can also reduce people’s trust in true news 27 . Recent studies found that users often read the comment sections before the article itself 28 and about 20% spend more time reading comments than the article 29 . This could be explained by people focusing on the first available salient piece of information in the face of the overwhelming nature of social media 15 , 30 . Comments might be so engaging because they are exemplar, anecdotal opinion cues that make an abstract issue more concrete, vivid and easy to comprehend 6 . Such examples can be perceived as representing the majority’s beliefs, which people tend to consider more accurate (via the “bandwagon effect” 31 ). When people adjust their judgments to the majority’s beliefs, social conformity can ensue 6 , 26 . However, comment sections can represent a distorted picture of the public sentiment 32 . Individuals with majority-conform views tend to voice their perspectives more than individuals with incongruent views, as the latter fear repercussions and public shame. This process can lead over time to a marginalization of minority views 33 , polarizing the public discourse by making people less tolerant to discordant opinions 34 – 36 . Unfortunately, commenters appear to be a limited, non-representative and possibly biased sample of the general population 34 : US commenters tend to be less educated than passive readers 29 , German commenters are older 5 and hold more conservative ideologies 37 than passive readers, and men write more comments than women 38 . As an example, negative and skeptic messages towards vaccines were a large proportion of the content spread on social media during the COVID-19 pandemic 39 . These messages created a fertile ground for the consolidation of anti-vaccination echo chambers 40 , the polarization of users’ attitudes 41 , and had a crucial role in shaping people’s perception towards government measures, therefore having a tangible impact on society 39 , 40 . To counteract these influences, research efforts are increasingly trying to identify protective factors, such as improvements of individuals’ maturity or literacy in using digital technologies 12 , 14 . Digital maturity can be conceptualised as a multi-dimensional construct encompassing attitudes and capabilities that individuals need in order to thrive in online environments and potentially shield themselves against digital threats e.g., 42 . To quantify digital maturity, Laaber and colleagues developed a Digital Maturity Index (DIMI), with three “capacities”: The capacity to use digital technologies in an autonomous and self-determined way; the capacity to master increasing digital challenges and solve problems; and the capacity to interact adequately with others and to contribute to society 42 . How this construct relates to social influence on opinion formation is an highly relevant question that had not been addressed so far. In this study, we aimed to develop a task to experimentally replicate and quantify the impact of user-generated comments on the formation of personal opinions regarding news posted on social media. We further aimed to assess whether two factors would reduce the social influence of these comments: (i) pre-existing attitudes toward the topics addressed in the news and (ii) digital maturity. We based our work on previous work that showed how comments posted at the end of blog articles and online news webpages shape people’s perception and attitude e.g., 6,43 . Those early studies often relied on between-subject designs using a single topic per condition, with limited assessment of participants’ pre-existing attitudes or initial opinions toward the news content. Some also employed complex or cluttered stimuli in attempts to mimic real social media environments, potentially introducing confounds, or in contrast presented the body of the article before the comments, which is a different format than current social media platforms. Several more recent studies have tested the impact on participants’ opinions of comments and other user responses to Facebook posts e.g., 21,26,32,44–46 , a popular platform widely discussed in the context of the social influence of user’s responses on opinions 22 , 40 , 41 . Informed by these studies, our study assessed the effect of user comments on participants’ opinions about multiple real news posts on Facebook, in a within-subjects design that systematically measured general attitudes toward the topic, initial opinions about each news item, and the impact of 4 comments that were either all supportive, all negative, or two supportive and two negative. This allowed us to precisely quantify opinion change in response to user-generated comments and identify individual factors moderating this effect. The study design is shown in Fig. 1 , and example stimuli are shown in Fig. 2 . We tested the following four pre-registered hypotheses. First, participants would adjust their personal opinions in the direction of the sentiment of the comments, which could be supportive, critical or mixed. Second, greater shifts in personal opinions would follow comments incongruent rather than congruent to participants’ opinions. Third, the strength of participants’ pre-existing attitudes towards the topics would modulate the magnitude of opinion adjustment, with weaker attitude towards the topics leading to larger opinion changes. Fourth, participants’ confidence in their opinion would diminish more following comments incongruent rather than congruent with their own opinion. In a fifth, not pre-registered hypothesis, we expected that higher digital maturity would be associated with smaller social influence of online comments. Results Effects of comment valence on opinion In both studies, participants significantly adjusted their second rating to conform with the other users’ opinions (see Fig. 3 a left and right for Study I and II, respectively). Specifically, participants shifted their opinions in the direction of the social information both after reading supportive (Study I: V = 3395, p 100; Study II: V = 4812, p < .001, BF = 41.88) and opposing comments (Study I: V = 9880, p = .001, BF = 56.48; Study II: V = 11538, p 100). Additionally, as we were expecting from the control condition, participants did not statistically change their opinion after reading mixed comments (Study I: V = 7573, p = .8, BF = 0.08; Study II: V = 7923, p = .4, BF = 0.078). Next, we computed opinion adjustment as the difference between the second and first opinion ratings (i.e. 𝑅 2 −𝑅 1 ). In both studies, we observed a negative adjustment following exposure to opposing comments (Study I: M = -0.69, SD = 2.59, 95% CI [-1.06, -0.32]; Study II: M = -0.93, SD = 3.48, 95% CI [-1.38, -0.47]) and a positive adjustment when exposed to supportive comments (Study I: M = 0.84, SD = 1.97, 95% CI [0.56, 1.12]; Study II: M = 0.62, SD = 2.52, 95% CI [0.28, 0.95]). However, the rating remained largely unchanged after being exposed to mixed comments (Study I: M = -0.05, SD = 2.16, 95% CI [-0.35, 0.25]; Study II: M = -0.6, SD = 3.32, 95% CI [-0.50, 0.37]) (see Fig. 3 b left and right for Study I and II, respectively). Indeed, the valence of the comments that participants were exposed to significantly predicted the opinion adjustments in both studies (Study I: F (386) = 23.86, p 100; Study II: F (440) = 13.76, p 100), such that: opposing comments towards the news item significantly predicted subsequent negative opinion shifts (Study I: ໿β = -0.65, SE = 0.22, 95% CI [-1.09, -0.21], t (386) = -2.929, p = .003; Study II: β = -0.87, SE = 0.29, 95% CI [-1.44, -0.29], t (440) = -2.949, p = .003), whereas supportive comments significantly predicted subsequent positive opinion shifts (Study I: ໿β = 0.91, SE = 0.22, 95% CI [0.47, 1.35], t (386) = 4.081, p = .0001; Study II: β = 0.69, SE = 0.29, 95% CI [0.11, 1.27], t (440) = 2.33, p = .02), compared to mixed comments. Effects of congruence between initial opinion and comment valence on opinion In this analysis, our objective was to evaluate whether participants would exhibit a greater inclination to adjust their opinions when exposed to comments that were incongruent with their first opinion rating (either R 1 > 0 followed by opposing comments, or R 1 0 followed by supportive comments, or R 1 < 0 followed by opposing comments). The rare trials in which participants’ R 1 was equal to 0 were excluded from this analysis (18 of 582 = 3.1% of trials in Study I, and 12 of 663 = 1.8% of trials in Study II). Opinion adjustment (i.e. |𝑅 2 −𝑅 1 |) changed gradually as a function of congruence: in both studies, we found the smallest opinion adjustment following exposure to congruent comments (Study I: M = 0.831, SD = 1.04, 95% CI [0.67, 0.98]; Study II: M = 1.25, SD = 1.77, 95% CI [1.02, 1.48]), the largest following exposure to incongruent comments (Study I: M = 1.63, SD = 2.64, 95% CI [1.26, 1.99] ; Study II: M = 2.10, SD = 3.29, 95% CI [1.66, 2.54]), and intermediate following exposure to control comments (Study I: M = 0.99, SD = 1.92, 95% CI [0.71, 1.26]; Study II: M = 1.76, SD = 2.81, 95% CI [1.39, 2.13]) (see Fig. 4 ). Indeed, the congruence between the participants initial opinion and the comments predicted the subsequent opinion adjustment in both studies (Study I: F (386) = 9.54, p < .0001; Study II: F (440) = 6.33, p = .0002): incongruent information led to significantly greater opinion adjustment compared to congruent information (Study I: β = 0.82, SE = 0.20, 95% CI [0.43, 1.22], t (386) = 4.09, p < .001; Study II: β = 0.833, SE = 0.24,, 95% CI [0.37, 1.30], t (440 ) = 3.53, p < .001). The magnitude of adjustment significantly differed between congruent information and control in Study II but not in Study I (Study I: β = 0.17, SE = 0.20, 95% CI [-0.22, 0.57], t (386) = 0.87 p = .39; Study II: β = 0.50, SE = 0.22, 95% CI [0.07, 0.94], t (440) = 2.26, p = .024); and the difference between incongruent information and control significantly differed in Study I but not Study II (Study I: β = 0.65, SE = 0.19, 95% CI [0.27, 1.03], t (386) = 3.36, p < .001; Study II: β = 0.33, SE = 0.22, 95% CI [-0.11, 0.77], t (440) = 1.49, p = .138). Note that while we aimed to test participants on all three types of comment congruence (congruent, incongruent and control), the comments were selected based on the participant’s attitude towards the topic of the headlines prior to exposition to the headlines. In some cases, a participant’s first opinion was not aligned with their attitude, which resulted in the subsequent comments being congruent instead of the intended incongruent (or vice-versa), and thus this participant was tested more often in one of the congruence conditions than in the others. These cases appear as vertical lines in Fig. 4 . Effects of congruence between initial opinion and comments on confidence adjustment Next, we aimed to assess the impact of the type of comments on participants’ confidence in their opinion. Specifically, we tested whether participants’ confidence would increase with information that aligned with their opinion compared to information that did not align with it. Using confidence adjustment (i.e. |C 2 − C 1 |) as dependent variable, we found that in both studies the congruence of the first opinion rating and the comments’ valence predicted confidence adjustment (Study I: F (2,386) = 5.94, p = .003; Study II: F (2,440) = 8.55, p < .001). Specifically, confidence decreased after incongruent information compared to congruent information (Study I: β = -4.44, SE = 1.29, 95% CI [-6.99, -1.90], t (386) = -3.427, p < .001; Study II: β = -5.85, SE = 1.52, 95% CI [-8.82, -2.88], t (440) = -3.86, p < .001). In Study I but not in Study II, there was a significant decrease in confidence adjustment from control to incongruent information (Study I: β = -2.54, SE = 1.27, 95% CI [-5.03, -0.05], t (386) = -2.00, p = .046; Study II: β = -1.01, SE = 1.52, 95% CI [-3.99, 1.98], t (386) = -0.66, p = .81), and in Study II but not in Study I there was a significant increase in confidence adjustment from control to congruent information (Study I: β = 1.90, SE = 1.30, 95% CI [-0.65, 4.46], t (386) = 1.46, p = .144; Study II: β = 4.84, SE = 1.51, 95% CI [1.88, 7.80], t (440) = 3.20, p = .001) (see Fig. 5 ). Effects of pre-existing attitudes towards the topics of the news headlines on opinion adjustment Next, we assessed whether having a stronger pre-existing attitude towards the three topics of the task would reduce participants’ opinion adjustment. Indeed, we found that participants adjusted their opinions less when they had stronger pre-existing attitudes compared to when they had weaker pre-existing attitudes towards the topics ( Nonparametric Kendall's tau (τ) correlation test; Study I: R = -0.07, p = .015; Study II: R = -0.09, p < .001). In an additional exploratory analysis we assessed whether people would be more prone to opinion resistance in one of the three topics, but did not find any such effect in either study (Study I: F (2,386) = 1.37, p > .05; Study II: F (2,440) = 0.01, p > .05). Effects of initial confidence on opinion adjustment In this exploratory analysis, we assessed if the amount of confidence participants had in their first opinion could influence opinion adjustment. Indeed, in both studies we found that participants with higher confidence in their initial opinions were less likely to adjust their opinion after reading the comments ( Nonparametric Kendall's tau (τ) correlation test; Study I: R = -0.2, p < .001; Study II: R = -0.14, p < .001). Effects of digital maturity on opinion adjustment In Study II, an additional (exploratory) aim was to assess whether more digitally mature participants were less susceptible to the influence of others’ opinions. We found that participants adjusted their opinion in the direction of the social information more when their total score in the digital maturity index (DIMI) was lower (indicating lower digital maturity) rather than higher ( Nonparametric Kendall's tau (τ) correlation test; R = -0.1, p < .001; Fig. 7 ). To investigate this relationship further, we re-ran the correlation test to assess relations between opinion adjustment and each of the three capacities of the DIMI, and found significant (but weaker) correlations with the capacity to master challenges ( R = -0.064, p = .016) and the capacity to interact adequately ( R = -0.061, p = .022), but not with the capacity to use digital technologies in an autonomous way ( R = -0.035, p = .19). Effects of demographic variables Lastly, we examined the relationships between opinion adjustment and demographic variables including age , sex , education , political ideology , Facebook usage , social media usage , engagement with comments on social media , and social media checking using linear mixed models. In Study I, we found that participants with more conservative political views (high scores on political ideology ) adjusted their opinion more compared to those with a more liberal political ideology ( F (1,192) = 5.96, p = .0155); participants who engaged more frequently with Facebook in their everyday life were more susceptible to other people’s comments compared to those who used Facebook less frequently ( F (5,188) = 3.2, p = .0085); and participants who engaged more frequently with the comments section were also more susceptible to other people’s comments ( F (3,190) = 2.98 p = .033). In Study II, none of these relationships reached significance. Discussion We aimed to develop a task that experimentally demonstrates the impact of user comments on opinion formation about news presented online, while addressing limitations of past studies. We found that participants consistently updated their opinions to align with the sentiment of the comments, with stronger pre-existing attitudes and higher personal confidence in one's prior opinion reducing the degree of change. Our findings are consistent with previous reports showing that individuals often adjust their opinions to align with the majority view after reading user comments 6 , 23 , 26 , 43 , 46 – 48 . Opinion shifts occurred after reading sets of consistently supportive or opposing comments, but not after reading mixed comments. This is consistent with previous research suggesting that a dominant group opinion leads to stronger opinion shifts 46 . Unlike earlier studies that suggested negative comments had a more substantial impact, our research found that both positive and negative comments significantly influenced opinions. This could be due to our use of civil, argumentative comments, as these types of comments can signal expertise and thus are more persuasive than subjective comments 23 , 44 . This finding supports the promotion of civil discourse on social media, which can enhance constructive discussion while preserving the credibility of news articles. We also found that participants were more influenced by comments that did not align with their own initial opinion, consistent with previous findings 47 . Specifically, opinion adjustment was largest following comments incongruent with participants’ original opinion, intermediate following mixed comments, and smallest following comments congruent with participants’ original opinion. This finding aligns with Cognitive Dissonance Theory 49 , which suggests that people may adjust their views to reduce discomfort when confronted with conflicting opinions. Additionally, we found that stronger pre-existing attitudes were associated with smaller shifts in opinion, which replicates previous findings of perseverance when individuals hold strong beliefs about a topic 47 , 48 . Confidence in one's opinion played a significant role in opinion change. Participants less confident in their views were more susceptible to influence. This trend aligns with previous research suggesting that lower confidence increases susceptibility to external influence 46 , 50 . Interestingly, we also found that participants’ confidence in their opinion decreased after reading information incongruent with their own opinion. Some demographic factors influenced susceptibility to social media comments. Frequent Facebook users and individuals who engaged with comment sections were more likely to adjust their opinions based on online comments, as were more conservative participants. These findings point to the potential risks of heavy social media consumption undermining individuals' ability to form independent opinions. However, the fact that these effects were small and only significant in Study I suggests that these influences are relatively minor. In contrast, digital maturity, specifically the capacities to use digital technologies in an autonomous and self-determined way and to master increasing digital challenges and solve problems, were associated with reduced social influence. This suggests that greater awareness of online content helps individuals critically evaluate the information they encounter. This study has limitations. It was run in a controlled experimental setting, which allowed us to test our hypotheses cleanly but does of course not capture the complexity of real-world interactions. Although we aimed to expose all participants to an equal number of congruent, incongruent and control comments, these comments were selected based on participants’ attitude towards the topic of the headlines, and thus some participants ended up being exposed to more incongruent sets of comments than congruent ones, or vice-versa. While we do not believe this to be a serious issue, a more adaptive experiment code may remedy this problem in future versions of this experiment. Also, due to our use of a -7 to 7 scale for opinions, the magnitude of possible adjustment was larger for opinion changes crossing 0 than for changes remaining on the same side of 0. While this may have led to a higher sensitivity to opinion adjustments switching from opposing to supportive (or vice-versa) than to smaller opinion adjustments, the former opinion switches were arguably the most interesting adjustments in our experiment. The specific, polarizing topics used may limit generalizability, and future research should explore less controversial topics. We assessed the impact of pre-existing attitudes but other individual factors should be considered in future studies. The lack of longitudinal data limits understanding of the long-term effects of opinion change. Additionally, the explicit nature of the main question may introduce demand characteristics, though the online setting mitigates this concern. Future research could use more implicit measures. Social media, while enhancing connectivity, present challenges for societal decision-making and democracy. This project, initiated during the COVID-19 pandemic, highlights how social media fuel misinformation and influence public behaviour, particularly regarding vaccine safety and health guidelines 39 . The findings underscore the need for better regulation and moderation of these platforms. Firstly, social media platforms must be held accountable for opaque algorithms and inadequate content moderation. Secondly, stricter regulations are needed to curb misleading and harmful content while promoting civil discourse. Lastly, improving digital literacy is crucial, particularly for younger generations, to help them critically evaluate online content and resist manipulation. Educating the public, especially students, will foster a more informed and resilient population, reducing the influence of online misinformation and fostering mutual understanding in society. Methods Overview Study I was run between September and October 2021; Study II was run on 20 October 2022. Before collecting data, we pre-registered our planned study design, hypotheses and statistical analyses on OSF ( https://osf.io/5dm7h , date of pre-registration: September 20, 2021). Deviations from the pre-registered statistical analyses as well as exploratory analyses that were not pre-registered are clearly mentioned in the Results section. Ethics statement This research was performed in accordance with the Declaration of Helsinki, was performed in accordance with the relevant guidelines and regulations, and was approved by the Ethics Committee of the Medical Faculty of the University of Bonn (Approval Number Az. 419/21). Written informed consent was obtained from all participants. Participants For Study I, we recruited 240 American participants from Amazon MTurk, who completed the experiment through their web browsers. The number of participants was determined based on a power analysis in G*Power 3 51 aiming to compare within-subject the means of the opinions before and after reading comments using a Wilcoxon signed-rank test (matched pairs), with a medium effect size of 0.24 (determined in a pilot study; see Supplementary Information), 0.05 alpha error probability, and 0.95 power, which prescribed a sample size of 228 participants. Participants were excluded if they moved through the survey at an unrealistic pace (e.g. if they took less than 3 seconds to read the comments); failed to properly answer the attention checks within the task; filled in the attitudinal questionnaire inconsistently (e.g. always giving the same answer even to reverse items). Easy attention checks were also included in the instructions, allowing the platform Qualtrics to automatically exclude participants who failed to correctly answer them. Based on these criteria, we excluded 46 datasets, leaving n = 194 participants (80 female; 9 between 18–25 years old, 33 between 26–30 years old; 91 between 31–40 years old, 61 above 40 years old) in the statistical analyses. For Study II, planned according to the same power analysis as Study I, we recruited 221 individuals (143 female; ໿147 between 18–25 years old; 53 between 26–30 years old; 21 above 31 years old) through the database of the BonnEconLab (University of Bonn, Germany) for the same online experiment. Registration in this database is voluntary and the pool is mainly made of University of Bonn students and staff. Participants were at least 18 years old and fluent in the English language (approximately B2 in the Common European Framework of Reference). Only one dataset was excluded based on the same criteria as Study I. Experimental procedure Studies 1 and 2 shared the same study design and experimental procedure, except that in Study II, we additionally collected answers to the DIMI questionnaire 42 . Participants started the experiment by reading the instructions which explained the purpose of the study and the exclusion criteria. After reading the instructions, participants completed validated attitudinal questionnaires about the three contemporary topics used in the task (see Pre-Existing Attitudes, below). Each participant was then presented with 3 out of 9 possible news headlines – one headline per topic (see Opinions about news headlines below, and see Supplementary Information for all headlines, comments and task development). Participants gave their opinions about the headlines before and after reading the comments. In Study II, participants then completed the DIMI questionnaire. At the end of the task, we gathered demographics data (sex and gender, age, education, political affiliations, Facebook and social media general usage). The study lasted between 20 and 25 minutes and participants were compensated with 6€ for their time. Pre-existing attitudes Participants completed three attitudinal questionnaires, about climate change 52 , vaccination 53 and veganism 54 . The scores obtained from these three questionnaires were used to classify participants as either supporting (if their score was above 50% of the maximum score) or opposing each topic. This classification was in turn used to determine which comments participants were be shown (see Comments, below). These scores were also z-scored and used as predictors of opinion change (see Statistical Analysis, below). Opinions about news headlines After completing the attitudinal questionnaires, participants were presented with a news headline without comments. The headlines were gathered from Facebook posts (see Supplementary Information). Participants then indicated their opinion about the content of the news headline using a slider ranging from − 7 (= strongly oppose) to + 7 (= strongly support). This rating was coded as R 1 , indicating participants’ “prior opinion” (prior to comments) about the headline. Participants then rated their confidence in this opinion on a scale from 0 (= not confident at all) to 100 (= absolutely confident). This first confidence rating was coded as C 1 . After this, four comments appeared below the same news headline (see Comments, below). Participants were instructed to carefully read these comments and then give their opinion and confidence a second time (coded as R 2 and C 2 respectively) (see Fig. 2 ). This procedure was repeated for the other two news headlines. The order in which the topics were presented was randomised. Comments Comments (also gathered from Facebook, see Supplementary Information) presented after each headline were of three types: four comments supportive of the headline, four opposing comments, or two supportive and two opposing comments. Whether participants were exposed to supportive opposing comments on a given headline depended on their pre-existing attitude towards the topics of the headline. Each participant was confronted once with each of: i) four comments congruent with their initial attitude towards the topic of the headline, ii) four comments incongruent with their initial attitude, and iii) two congruent comments and two incongruent comments. This was done to assess in each participant (i.e., within-subject) the impact of comment congruence on opinion adjustment and confidence in opinions, and was implemented in Qualtrics using partial randomization. The type of comments was the primary experimental within-subject manipulation. While we thus determined which comments were shown after each news headline before exposing participants to the headlines, after acquiring the data we classified each trial as congruent, incongruent or control based on the congruence between the participant’s prior opinion (R 1 ) and the comments shown (see “Effects of congruence …” section below). Statistical analyses Statistical analyses were conducted with R language (version 4.3.2) and RStudio (version 2023.12.1.402, Posit Team, 2024), as well as the software JASP (version 0.18.2, JASP Team, 2023) for correlation matrices. The R packages employed for data cleaning, analysis and visualization included: BayesFactor, cowplot, ggpubr, gmodels, Hmisc, kableExtra, lme4, nlme, plotrix, readxl, reshape2, see, tidyverse. All statistical analyses were conducted using a two-tailed test and a significance threshold of ໿α ≤ 0.05. All analyses described below were applied to the data of Study I and 2 except where indicated. Deviation from the pre-registration As stated in the pre-registration of Study I, we planned to solely employ one-way ANOVA tests to assess our main hypotheses. However, for both studies, we ultimately opted to run mixed-effects linear model analyses, due to the non-normal distribution of our data and because of the robustness of these models against assumption violations. This was a more detailed and flexible approach to modelling the relationship between dependent and independent variables, because it allowed analysing single-trial data and including both fixed and random effects, resulting in a more accurate and comprehensive interpretation of the data. Therefore, these models were considered most suitable to address our research objectives. Furthermore, to accommodate control trials in the congruence analysis (see below), we deviated from the pre-registration and considered the congruence index as an ordinal variable with three instead of two levels: congruent , control and incongruent . Effects of comment valence To determine the presence of a significant difference in opinion ratings before and after presenting the comments, as well as to assess the effectiveness of presenting mixed comments as a control condition, we performed three paired Wilcoxon tests (one for each level of the explanatory variable: supportive, opposing, mixed) between the first and the second opinion ratings. Subsequently, to examine whether the valence of the comments could predict the direction of the opinion shifts, opinion adjustments were computed (i.e., 𝑅 2 −𝑅 1 ) and a mixed-effects linear regression model was performed using the function lme from the R package lme4 . In this model, opinion adjustment (𝑅 2 −𝑅 1 ) was the dependent variable, the valence of the comments (i.e. supportive, opposing, mixed) was the explanatory variable, and random intercepts were included to account for individual differences in the average opinion ratings: (Eq. 1). Opinion adjustment ij = β 0 ​ + β 1 Comment valence ij + u j + ϵ ij The subscript j indexes participants, and the subscript i indexes observations. Opinion adjustment ij represents the dependent variable and Comment valence ij represents the independent variable. u j indicates participant-specific random intercepts and ϵ ij the residual. Effects of congruence between initial opinion and comment valence on opinion adjustment We aimed to evaluate the effect of the congruence between the participant’s initial opinion and the opinion expressed in the comments on opinion adjustment. Recall that initial opinion (R 1 ) was a number between − 7 and 7. Trials were labelled as “ congruent condition ” either when participants’ first opinion was supportive (R 1 > 0) and the comments’ valence were also supportive, or when participants’ first opinion was opposing (R 1 0) and the comments’ valence were opposing, or when participants’ first opinion was opposing (R 1 < 0) and comments were supportive. Trials in which comments were mixed were labelled as “ control condition ”. Trials in which R 1 was exactly 0 (this was the case in 12 of 582 = 2% of trials in Study 1, and in 7 of 663 = 1.1% of trials in Study 2) were excluded from the analysis. To investigate the influence of congruence on opinion adjustment, we ran a mixed-effects linear regression model. The dependent variable was the absolute value of opinion adjustment (i.e., |𝑅 2 −𝑅 1 |), and congruence was an explanatory ordinal variable with three levels ( congruent , control or incongruent ). Random intercepts were included to account for individual differences in the average opinion ratings: (Eq. 2). Opinion adjustment ij = β 0 ​ + β 1 Congruence ij + u j + ϵ ij Effects of congruence between initial opinion and comment valence on confidence adjustment We assessed the effect of the congruence between first opinion and comments on participants’ adjustment in confidence in their opinion. Confidence adjustments were computed as the difference between confidence in the second and the first opinions (i.e. C 2 − C 1 ). We ran a mixed-effects linear model with confidence adjustment as dependent variable and congruence as the independent ordinal variable, with three levels: congruent , control , incongruent . We used random intercepts to control for individual differences in the average confidence ratings: (Eq. 3). Confidence adjustment ij = β 0 ​ + β 1 Congruence ij + u j + ϵ ij Effects of pre-existing attitudes towards headline topics on opinion adjustment To investigate the relationship between pre-existing attitudes towards the topics of the headlines and opinion adjustment, we performed a linear regression with pre-existing attitude as the explanatory variable and opinion adjustment (|𝑅 2 −𝑅 1 |) as dependent variable. Due to the variations in the number of items and the different Likert scales across the three questionnaires, the resulting scores were standardised into z-scores to obtain a unique index of pre-existing attitudes that ranged from 0 (weak attitude towards the topic) to 50 (strong attitude towards the topic). The equation was: (Eq. 4). Opinion adjustment ij = β 0 ​ + β 1 Attitude score ij + ϵ ij Effects of digital maturity on opinion adjustment (Study II) To assess whether more digital mature individuals were less susceptible to the influence of others’ opinions, we ran correlation analyses between opinion adjustment (|𝑅 2 −𝑅 1 |) and the DIMI 42 with its three sub-categories: The capacity to use digital technologies in an autonomous and self-determined way ; the capacity to master increasing digital challenges and solve problems ; and the capacity to interact adequately with others and to contribute to society . Assessing data sensitivity with Bayesian hypothesis testing To clarify whether non-significant results support hull hypothesis or simply reflect data insensitivity, we additionally calculated Bayes Factors (BF) to assess the level of evidence for the alternative hypothesis over the null hypothesis. We used default priors in the R package BayesFactor and the often-used convention attributed to Harold Jeffreys (see Dienes 2014): a BF greater than 3 indicated that there was significant evidence for H1 over H0, while a BF less than 1/3 indicated that there was significant evidence for H0 over H1. A BF between 1/3 and 3 indicated that the data was insensitive to distinguish between H1 and H0. Declarations Data and code availability The stimuli, data and code for this study are openly available at https://osf.io/sv5wn/. The preregistration is at https://osf.io/5dm7h. Funding This work was funded by the European Union’s Horizon 2020 research and innovation programme [grant number 870578]. Author contributions F.N. : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing. 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Rep. 10 , 6840 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviews received at journal 28 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 22 Oct, 2025 Editor invited by journal 22 Oct, 2025 Submission checks completed at journal 20 Oct, 2025 First submitted to journal 20 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":174336,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental procedure. Participants filled out attitudinal questionnaires about each of the three topics (climate change, vaccinations, veganism) that the news headlines referred to \u003cstrong\u003e(left)\u003c/strong\u003e. At the start of each trial, participants read a news headline (see \u003cstrong\u003eFig. 2\u003c/strong\u003e), then gave their opinion and their confidence therein using two rating scales \u003cstrong\u003e(center)\u003c/strong\u003e. They then read four comments from other users and rated their opinion and confidence for a second time \u003cstrong\u003e(right)\u003c/strong\u003e. The procedure was repeated for each of the three topics.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/3e13f3343cbd8e652598d79b.png"},{"id":95663224,"identity":"4cbf2367-cc82-4547-9edd-3ad4f4480fa2","added_by":"auto","created_at":"2025-11-11 16:38:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":240058,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a news headline and the associated user comments.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/d0da87d3a870126dea469d45.png"},{"id":95663408,"identity":"1b0beade-062f-4423-9fe1-c87c36b38242","added_by":"auto","created_at":"2025-11-11 16:38:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":333219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Effect of the comments’ valence on participants’ second opinion rating in Study I (left) and Study II (right). The black dots are the mean values across participants, the grey dots are the individual mean values and the error bars represent the standard error of the mean, and the contours represent Kernel density plots \u003cstrong\u003e(b)\u003c/strong\u003e Opinion adjustments computed as the difference between the first and second opinion ratings in Study I (left) and Study II (right). The red dots are the mean values across participants and the contours represent Kernel density plots.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/542d72ca0409c26ee7111fea.png"},{"id":95663481,"identity":"e8e86b58-3f94-47f3-8d5b-56b29e57602d","added_by":"auto","created_at":"2025-11-11 16:38:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":281410,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of congruence of first opinion rating and valence of comments on opinion adjustment in Study I (left) and Study II (right). Note that some participants were tested more often in one of the congruence conditions (see explanation in main text), and that some participants almost completely reversed their opinion after reading the comments (update values close to 14). The black dots are mean values across participants, error bars represent standard errors of the mean. Red lines connect the mean values, grey dotted lines represent individual participants. *, p\u0026lt;0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/fde95eb520bcbeed6740e7f4.png"},{"id":95663346,"identity":"7c44e9f5-63e8-4a93-8fa6-b498b58b2d37","added_by":"auto","created_at":"2025-11-11 16:38:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":229601,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of congruence between first opinion and valence of comments on confidence adjustments in Study I (left) and Study II (right). Conventions as in Fig. 4.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/77006ed01f4cab3231dc6486.png"},{"id":95663369,"identity":"05e4547c-2c84-459b-925b-97c7e9301add","added_by":"auto","created_at":"2025-11-11 16:38:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":89459,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between attitude about the topic (assessed prior to reading the headlines using questionnaires) on opinion adjustment in Study I (left) and Study II (right).\u003cem\u003eR\u003c/em\u003e and \u003cem\u003ep\u003c/em\u003e values result from Kendall’s Tau correlation tests. Dots are single trials across all participants, the red line shows the best-fitting regression.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/8d75a8d6497f93e4a5f1044c.png"},{"id":95663279,"identity":"b8faab69-bc92-4c68-9dbd-13087817ec9f","added_by":"auto","created_at":"2025-11-11 16:38:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":351803,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between opinion adjustment and digital maturity (total score and the 3 Capacities; assessed as part of Study II). \u003cem\u003eR\u003c/em\u003e and \u003cem\u003ep\u003c/em\u003e values result from Kendall’s Tau correlation tests.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/fae3bd54f93e51f1f094ea3d.png"},{"id":95664099,"identity":"48c2401e-eab0-4e96-9369-0ad54fd30468","added_by":"auto","created_at":"2025-11-11 16:39:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2184260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/5212436b-3e21-443e-b8fa-2b0bb599f5ba.pdf"},{"id":95663338,"identity":"fbd48608-e0bc-45fd-9478-c46f5387d648","added_by":"auto","created_at":"2025-11-11 16:38:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":53870,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7876614/v1/fb2f6b38796af84f84600d8f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of social media comments on opinions about news","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Internet radically transformed how we interact and communicate with each other \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As of July 2025, about 5.76\u0026nbsp;billion people (70.0% of the global population) are active mobile internet users, and most spend time on social media sites \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These developments have resulted in online newspapers on social media becoming the main source of daily news \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, with more news being consumed via online media than all other sources in the USA for example \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Characteristic of online news is the audience\u0026rsquo;s ability to comment on the content, usually immediately below the post, which fosters interactivity and discussion among readers \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Reading news thus becomes a collective activity \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e with the expression of a broader range of opinions and exponentially growing debate.\u003c/p\u003e\u003cp\u003ePopular on social media sites are \u0026ldquo;snack news\u0026rdquo;, a format requiring low cognitive engagement and consisting of a news headline, a picture and a short preview of the news article \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. While snack news are designed to only convey a general overview of a topic without gaining in-depth knowledge of it, Bakshy and colleagues found that just 7% of 10.1\u0026nbsp;million Facebook users clicked on the links to full articles about political and world affairs in their news feed \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. As a result, readers feel informed about an issue without actual knowledge gain and might express stronger convictions on these topics than fully informed readers \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSince the rise of online news consumption, disinformation and misinformation have become a crucial contemporary problem, and were ranked in a 2025 survey of 1500 experts by the World Economic Forum as the top global risk during the next 2 years \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This is particularly concerning when these techniques are used to undermine established scientific knowledge, as observed in current debates on epidemics, vaccines or climate change \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A recent meta-analysis of over 300 studies of over 190\u0026rsquo;000 participants reports that participants seem to be overall skeptical of the veracity of online news and can detect false news when prompted to do so, but skepticism increases for news that are discordant with a reader\u0026rsquo;s political views, and skepticism may also lead a reader to not believe reliable information enough \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A recent review concludes that cognitive ability, thinking styles, and metacognitive scrutiny protect from misinformation, while early adverse experiences, ideology, and the removal of fact-checking on social media platforms facilitate it \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSnack news lend themselves particularly well to disinformation and misinformation campaigns. Clickbait headlines with emotional language and dramatic images draw attention and strong reactions such as outrage and indignation, which increase engagement metrics that speed up content spreading, including fake content, at the detriment of the moderate and balanced perspectives of professionally curated content \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Such campaigns were shown to have real-world impact on the 2016 US presidential election and the UK Brexit referendum for example \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Similarly, Facebook and YouTube contributed to the rise and unification of far right-wing parties in US and Germany through recommendation of polarising and conspiracist material \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSocial cues including user-generated comments probably play a large role in the spread of dis- and misinformation \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Indeed, laypeople without proper expertise or reputation can express their opinions, emotions and reactions on social media and potentially reach as many users as respectable news sources \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Additionally, shallow and disrespectful comments can have negative repercussions on the material they are attached to, including on the perceived quality of professional and scientific articles \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Critical comments are powerful: they can be more effective at flagging fake-news than official disclaimers attached to the post made by the platforms themselves \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, but can also reduce people\u0026rsquo;s trust in true news \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent studies found that users often read the comment sections before the article itself \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and about 20% spend more time reading comments than the article \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This could be explained by people focusing on the first available salient piece of information in the face of the overwhelming nature of social media \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Comments might be so engaging because they are exemplar, anecdotal opinion cues that make an abstract issue more concrete, vivid and easy to comprehend \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Such examples can be perceived as representing the majority\u0026rsquo;s beliefs, which people tend to consider more accurate (via the \u0026ldquo;bandwagon effect\u0026rdquo; \u003csup\u003e31\u003c/sup\u003e). When people adjust their judgments to the majority\u0026rsquo;s beliefs, social conformity can ensue \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, comment sections can represent a distorted picture of the public sentiment \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Individuals with majority-conform views tend to voice their perspectives more than individuals with incongruent views, as the latter fear repercussions and public shame. This process can lead over time to a marginalization of minority views \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, polarizing the public discourse by making people less tolerant to discordant opinions \u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Unfortunately, commenters appear to be a limited, non-representative and possibly biased sample of the general population \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e: US commenters tend to be less educated than passive readers \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, German commenters are older \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and hold more conservative ideologies \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e than passive readers, and men write more comments than women \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. As an example, negative and skeptic messages towards vaccines were a large proportion of the content spread on social media during the COVID-19 pandemic \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These messages created a fertile ground for the consolidation of anti-vaccination echo chambers \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, the polarization of users\u0026rsquo; attitudes \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and had a crucial role in shaping people\u0026rsquo;s perception towards government measures, therefore having a tangible impact on society \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo counteract these influences, research efforts are increasingly trying to identify protective factors, such as improvements of individuals\u0026rsquo; maturity or literacy in using digital technologies \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Digital maturity can be conceptualised as a multi-dimensional construct encompassing attitudes and capabilities that individuals need in order to thrive in online environments and potentially shield themselves against digital threats \u003csup\u003ee.g., 42\u003c/sup\u003e. To quantify digital maturity, Laaber and colleagues developed a Digital Maturity Index (DIMI), with three \u0026ldquo;capacities\u0026rdquo;: The capacity to use digital technologies in an autonomous and self-determined way; the capacity to master increasing digital challenges and solve problems; and the capacity to interact adequately with others and to contribute to society \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. How this construct relates to social influence on opinion formation is an highly relevant question that had not been addressed so far.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to develop a task to experimentally replicate and quantify the impact of user-generated comments on the formation of personal opinions regarding news posted on social media. We further aimed to assess whether two factors would reduce the social influence of these comments: (i) pre-existing attitudes toward the topics addressed in the news and (ii) digital maturity. We based our work on previous work that showed how comments posted at the end of blog articles and online news webpages shape people\u0026rsquo;s perception and attitude \u003csup\u003ee.g., 6,43\u003c/sup\u003e. Those early studies often relied on between-subject designs using a single topic per condition, with limited assessment of participants\u0026rsquo; pre-existing attitudes or initial opinions toward the news content. Some also employed complex or cluttered stimuli in attempts to mimic real social media environments, potentially introducing confounds, or in contrast presented the body of the article before the comments, which is a different format than current social media platforms. Several more recent studies have tested the impact on participants\u0026rsquo; opinions of comments and other user responses to Facebook posts \u003csup\u003ee.g., 21,26,32,44\u0026ndash;46\u003c/sup\u003e, a popular platform widely discussed in the context of the social influence of user\u0026rsquo;s responses on opinions \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Informed by these studies, our study assessed the effect of user comments on participants\u0026rsquo; opinions about multiple real news posts on Facebook, in a within-subjects design that systematically measured general attitudes toward the topic, initial opinions about each news item, and the impact of 4 comments that were either all supportive, all negative, or two supportive and two negative. This allowed us to precisely quantify opinion change in response to user-generated comments and identify individual factors moderating this effect. The study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and example stimuli are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe tested the following four pre-registered hypotheses. First, participants would adjust their personal opinions in the direction of the sentiment of the comments, which could be supportive, critical or mixed. Second, greater shifts in personal opinions would follow comments incongruent rather than congruent to participants\u0026rsquo; opinions. Third, the strength of participants\u0026rsquo; pre-existing attitudes towards the topics would modulate the magnitude of opinion adjustment, with weaker attitude towards the topics leading to larger opinion changes. Fourth, participants\u0026rsquo; confidence in their opinion would diminish more following comments incongruent rather than congruent with their own opinion. In a fifth, not pre-registered hypothesis, we expected that higher digital maturity would be associated with smaller social influence of online comments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eEffects of comment valence on opinion\u003c/p\u003e\u003cp\u003eIn both studies, participants significantly adjusted their second rating to conform with the other users\u0026rsquo; opinions (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea left and right for Study I and II, respectively). Specifically, participants shifted their opinions in the direction of the social information both after reading supportive (Study I: \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3395, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;100; Study II: \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4812, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;41.88) and opposing comments (Study I: \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9880, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;56.48; Study II: \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11538, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;100). Additionally, as we were expecting from the control condition, participants did not statistically change their opinion after reading mixed comments (Study I: \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7573, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.8, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08; Study II: \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7923, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.4, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.078). Next, we computed opinion adjustment as the difference between the second and first opinion ratings (i.e. \u0026#119877;\u003csub\u003e2\u003c/sub\u003e\u0026minus;\u0026#119877;\u003csub\u003e1\u003c/sub\u003e). In both studies, we observed a negative adjustment following exposure to opposing comments (Study I: \u003cem\u003eM\u003c/em\u003e = -0.69, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.59, 95% \u003cem\u003eCI\u003c/em\u003e [-1.06, -0.32]; Study II: \u003cem\u003eM\u003c/em\u003e = -0.93, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.48, 95% \u003cem\u003eCI\u003c/em\u003e [-1.38, -0.47]) and a positive adjustment when exposed to supportive comments (Study I: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.84, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.97, 95% \u003cem\u003eCI\u003c/em\u003e [0.56, 1.12]; Study II: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.52, 95% \u003cem\u003eCI\u003c/em\u003e [0.28, 0.95]). However, the rating remained largely unchanged after being exposed to mixed comments (Study I: \u003cem\u003eM\u003c/em\u003e = -0.05, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.16, 95% \u003cem\u003eCI\u003c/em\u003e [-0.35, 0.25]; Study II: \u003cem\u003eM\u003c/em\u003e = -0.6, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.32, 95% \u003cem\u003eCI\u003c/em\u003e [-0.50, 0.37]) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb left and right for Study I and II, respectively). Indeed, the valence of the comments that participants were exposed to significantly predicted the opinion adjustments in both studies (Study I: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;23.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;100; Study II: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;13.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001, \u003cem\u003eBF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;100), such that: opposing comments towards the news item significantly predicted subsequent negative opinion shifts (Study I: \u003cem\u003e໿β\u003c/em\u003e = -0.65, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22, 95% \u003cem\u003eCI\u003c/em\u003e [-1.09, -0.21],\u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e = -2.929, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003; Study II: \u003cem\u003eβ\u003c/em\u003e = -0.87, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29, 95% \u003cem\u003eCI\u003c/em\u003e [-1.44, -0.29], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e = -2.949, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003), whereas supportive comments significantly predicted subsequent positive opinion shifts (Study I: \u003cem\u003e໿β\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22, 95% \u003cem\u003eCI\u003c/em\u003e [0.47, 1.35], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.081, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0001; Study II: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29, 95% \u003cem\u003eCI\u003c/em\u003e [0.11, 1.27], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02), compared to mixed comments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEffects of congruence between initial opinion and comment valence on opinion\u003c/p\u003e\u003cp\u003eIn this analysis, our objective was to evaluate whether participants would exhibit a greater inclination to adjust their opinions when exposed to comments that were incongruent with their first opinion rating (either R\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0 followed by opposing comments, or R\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0 followed by supportive comments), as opposed to comments that were congruent with their first opinion (either R\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0 followed by supportive comments, or R\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0 followed by opposing comments). The rare trials in which participants\u0026rsquo; R\u003csub\u003e1\u003c/sub\u003e was equal to 0 were excluded from this analysis (18 of 582\u0026thinsp;=\u0026thinsp;3.1% of trials in Study I, and 12 of 663\u0026thinsp;=\u0026thinsp;1.8% of trials in Study II). Opinion adjustment (i.e. |\u0026#119877;\u003csub\u003e2\u003c/sub\u003e\u0026minus;\u0026#119877;\u003csub\u003e1\u003c/sub\u003e|) changed gradually as a function of congruence: in both studies, we found the smallest opinion adjustment following exposure to congruent comments (Study I: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.831, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.04, 95% \u003cem\u003eCI\u003c/em\u003e [0.67, 0.98]; Study II: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.77, 95% \u003cem\u003eCI\u003c/em\u003e [1.02, 1.48]), the largest following exposure to incongruent comments (Study I: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.63, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.64, 95% \u003cem\u003eCI\u003c/em\u003e [1.26, 1.99] ; Study II: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.10, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.29, 95% \u003cem\u003eCI\u003c/em\u003e [1.66, 2.54]), and intermediate following exposure to control comments (Study I: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.92, 95% \u003cem\u003eCI\u003c/em\u003e [0.71, 1.26]; Study II: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.76, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.81, 95% \u003cem\u003eCI\u003c/em\u003e [1.39, 2.13]) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Indeed, the congruence between the participants initial opinion and the comments predicted the subsequent opinion adjustment in both studies (Study I: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001; Study II: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;6.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0002): incongruent information led to significantly greater opinion adjustment compared to congruent information (Study I: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, 95% \u003cem\u003eCI\u003c/em\u003e [0.43, 1.22], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Study II: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.833, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24,, 95% \u003cem\u003eCI\u003c/em\u003e [0.37, 1.30], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(440 )\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The magnitude of adjustment significantly differed between congruent information and control in Study II but not in Study I (Study I: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, 95% \u003cem\u003eCI\u003c/em\u003e [-0.22, 0.57], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.87 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.39; Study II: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22, 95% \u003cem\u003eCI\u003c/em\u003e [0.07, 0.94], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.024); and the difference between incongruent information and control significantly differed in Study I but not Study II (Study I: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, 95% \u003cem\u003eCI\u003c/em\u003e [0.27, 1.03], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Study II: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22, 95% \u003cem\u003eCI\u003c/em\u003e [-0.11, 0.77], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.138). Note that while we aimed to test participants on all three types of comment congruence (congruent, incongruent and control), the comments were selected based on the participant\u0026rsquo;s attitude towards the topic of the headlines prior to exposition to the headlines. In some cases, a participant\u0026rsquo;s first opinion was not aligned with their attitude, which resulted in the subsequent comments being congruent instead of the intended incongruent (or vice-versa), and thus this participant was tested more often in one of the congruence conditions than in the others. These cases appear as vertical lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEffects of congruence between initial opinion and comments on confidence adjustment\u003c/p\u003e\u003cp\u003eNext, we aimed to assess the impact of the type of comments on participants\u0026rsquo; confidence in their opinion. Specifically, we tested whether participants\u0026rsquo; confidence would increase with information that aligned with their opinion compared to information that did not align with it. Using confidence adjustment (i.e. |C\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026minus;\u0026thinsp;C\u003csub\u003e1\u003c/sub\u003e|) as dependent variable, we found that in both studies the congruence of the first opinion rating and the comments\u0026rsquo; valence predicted confidence adjustment (Study I: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2,386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003; Study II: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2,440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, confidence decreased after incongruent information compared to congruent information (Study I: \u003cem\u003eβ\u003c/em\u003e = -4.44, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.29, 95% \u003cem\u003eCI\u003c/em\u003e [-6.99, -1.90], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e = -3.427, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Study II: \u003cem\u003eβ\u003c/em\u003e = -5.85, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.52, 95% \u003cem\u003eCI\u003c/em\u003e [-8.82, -2.88], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e = -3.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In Study I but not in Study II, there was a significant decrease in confidence adjustment from control to incongruent information (Study I: \u003cem\u003eβ\u003c/em\u003e = -2.54, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, 95% \u003cem\u003eCI\u003c/em\u003e [-5.03, -0.05], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e = -2.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.046; Study II: \u003cem\u003eβ\u003c/em\u003e = -1.01, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.52, 95% \u003cem\u003eCI\u003c/em\u003e [-3.99, 1.98], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e = -0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.81), and in Study II but not in Study I there was a significant increase in confidence adjustment from control to congruent information (Study I: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.90, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.30, 95% \u003cem\u003eCI\u003c/em\u003e [-0.65, 4.46], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.144; Study II: \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.84, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.51, 95% \u003cem\u003eCI\u003c/em\u003e [1.88, 7.80], \u003cem\u003et\u003c/em\u003e\u003csub\u003e(440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEffects of pre-existing attitudes towards the topics of the news headlines on opinion adjustment\u003c/p\u003e\u003cp\u003eNext, we assessed whether having a stronger pre-existing attitude towards the three topics of the task would reduce participants\u0026rsquo; opinion adjustment. Indeed, we found that participants adjusted their opinions less when they had stronger pre-existing attitudes compared to when they had weaker pre-existing attitudes towards the topics (\u003cem\u003eNonparametric Kendall's tau (τ) correlation test;\u003c/em\u003e Study I: \u003cem\u003eR\u003c/em\u003e = -0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.015; Study II: \u003cem\u003eR\u003c/em\u003e = -0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In an additional exploratory analysis we assessed whether people would be more prone to opinion resistance in one of the three topics, but did not find any such effect in either study (Study I: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2,386)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05; Study II: \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2,440)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEffects of initial confidence on opinion adjustment\u003c/p\u003e\u003cp\u003eIn this exploratory analysis, we assessed if the amount of confidence participants had in their first opinion could influence opinion adjustment. Indeed, in both studies we found that participants with higher confidence in their initial opinions were less likely to adjust their opinion after reading the comments (\u003cem\u003eNonparametric Kendall's tau (τ) correlation test;\u003c/em\u003e Study I: \u003cem\u003eR\u003c/em\u003e = -0.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Study II: \u003cem\u003eR\u003c/em\u003e = -0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eEffects of digital maturity on opinion adjustment\u003c/p\u003e\u003cp\u003eIn Study II, an additional (exploratory) aim was to assess whether more digitally mature participants were less susceptible to the influence of others\u0026rsquo; opinions. We found that participants adjusted their opinion in the direction of the social information more when their total score in the digital maturity index (DIMI) was lower (indicating lower digital maturity) rather than higher (\u003cem\u003eNonparametric Kendall's tau (τ) correlation test; R =\u003c/em\u003e -0.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). To investigate this relationship further, we re-ran the correlation test to assess relations between opinion adjustment and each of the three capacities of the DIMI, and found significant (but weaker) correlations with the capacity to master challenges (\u003cem\u003eR =\u003c/em\u003e -0.064, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.016) and the capacity to interact adequately (\u003cem\u003eR =\u003c/em\u003e -0.061, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.022), but not with the capacity to use digital technologies in an autonomous way (\u003cem\u003eR =\u003c/em\u003e -0.035, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.19).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEffects of demographic variables\u003c/p\u003e\u003cp\u003eLastly, we examined the relationships between opinion adjustment and demographic variables including \u003cem\u003eage\u003c/em\u003e, \u003cem\u003esex\u003c/em\u003e, \u003cem\u003eeducation\u003c/em\u003e, \u003cem\u003epolitical ideology\u003c/em\u003e, \u003cem\u003eFacebook usage\u003c/em\u003e, \u003cem\u003esocial media usage\u003c/em\u003e, \u003cem\u003eengagement with comments on social media\u003c/em\u003e, and \u003cem\u003esocial media checking\u003c/em\u003e using linear mixed models. In Study I, we found that participants with more conservative political views (high scores on \u003cem\u003epolitical ideology\u003c/em\u003e) adjusted their opinion more compared to those with a more liberal political ideology (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,192)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0155); participants who engaged more frequently with Facebook in their everyday life were more susceptible to other people\u0026rsquo;s comments compared to those who used Facebook less frequently (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(5,188)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0085); and participants who engaged more frequently with the comments section were also more susceptible to other people\u0026rsquo;s comments (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(3,190)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.98 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.033). In Study II, none of these relationships reached significance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe aimed to develop a task that experimentally demonstrates the impact of user comments on opinion formation about news presented online, while addressing limitations of past studies. We found that participants consistently updated their opinions to align with the sentiment of the comments, with stronger pre-existing attitudes and higher personal confidence in one's prior opinion reducing the degree of change. Our findings are consistent with previous reports showing that individuals often adjust their opinions to align with the majority view after reading user comments \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Opinion shifts occurred after reading sets of consistently supportive or opposing comments, but not after reading mixed comments. This is consistent with previous research suggesting that a dominant group opinion leads to stronger opinion shifts \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUnlike earlier studies that suggested negative comments had a more substantial impact, our research found that both positive and negative comments significantly influenced opinions. This could be due to our use of civil, argumentative comments, as these types of comments can signal expertise and thus are more persuasive than subjective comments \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This finding supports the promotion of civil discourse on social media, which can enhance constructive discussion while preserving the credibility of news articles.\u003c/p\u003e\u003cp\u003eWe also found that participants were more influenced by comments that did not align with their own initial opinion, consistent with previous findings \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Specifically, opinion adjustment was largest following comments incongruent with participants’ original opinion, intermediate following mixed comments, and smallest following comments congruent with participants’ original opinion. This finding aligns with Cognitive Dissonance Theory \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, which suggests that people may adjust their views to reduce discomfort when confronted with conflicting opinions.\u003c/p\u003e\u003cp\u003eAdditionally, we found that stronger pre-existing attitudes were associated with smaller shifts in opinion, which replicates previous findings of perseverance when individuals hold strong beliefs about a topic \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eConfidence in one's opinion played a significant role in opinion change. Participants less confident in their views were more susceptible to influence. This trend aligns with previous research suggesting that lower confidence increases susceptibility to external influence \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Interestingly, we also found that participants’ confidence in their opinion decreased after reading information incongruent with their own opinion.\u003c/p\u003e\u003cp\u003eSome demographic factors influenced susceptibility to social media comments. Frequent Facebook users and individuals who engaged with comment sections were more likely to adjust their opinions based on online comments, as were more conservative participants. These findings point to the potential risks of heavy social media consumption undermining individuals' ability to form independent opinions. However, the fact that these effects were small and only significant in Study I suggests that these influences are relatively minor.\u003c/p\u003e\u003cp\u003eIn contrast, digital maturity, specifically the capacities to use digital technologies in an autonomous and self-determined way and to master increasing digital challenges and solve problems, were associated with reduced social influence. This suggests that greater awareness of online content helps individuals critically evaluate the information they encounter.\u003c/p\u003e\u003cp\u003eThis study has limitations. It was run in a controlled experimental setting, which allowed us to test our hypotheses cleanly but does of course not capture the complexity of real-world interactions. Although we aimed to expose all participants to an equal number of congruent, incongruent and control comments, these comments were selected based on participants’ attitude towards the topic of the headlines, and thus some participants ended up being exposed to more incongruent sets of comments than congruent ones, or vice-versa. While we do not believe this to be a serious issue, a more adaptive experiment code may remedy this problem in future versions of this experiment. Also, due to our use of a -7 to 7 scale for opinions, the magnitude of possible adjustment was larger for opinion changes crossing 0 than for changes remaining on the same side of 0. While this may have led to a higher sensitivity to opinion adjustments switching from opposing to supportive (or vice-versa) than to smaller opinion adjustments, the former opinion switches were arguably the most interesting adjustments in our experiment. The specific, polarizing topics used may limit generalizability, and future research should explore less controversial topics. We assessed the impact of pre-existing attitudes but other individual factors should be considered in future studies. The lack of longitudinal data limits understanding of the long-term effects of opinion change. Additionally, the explicit nature of the main question may introduce demand characteristics, though the online setting mitigates this concern. Future research could use more implicit measures.\u003c/p\u003e\u003cp\u003eSocial media, while enhancing connectivity, present challenges for societal decision-making and democracy. This project, initiated during the COVID-19 pandemic, highlights how social media fuel misinformation and influence public behaviour, particularly regarding vaccine safety and health guidelines \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The findings underscore the need for better regulation and moderation of these platforms. Firstly, social media platforms must be held accountable for opaque algorithms and inadequate content moderation. Secondly, stricter regulations are needed to curb misleading and harmful content while promoting civil discourse. Lastly, improving digital literacy is crucial, particularly for younger generations, to help them critically evaluate online content and resist manipulation. Educating the public, especially students, will foster a more informed and resilient population, reducing the influence of online misinformation and fostering mutual understanding in society.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eOverview\u003c/p\u003e\u003cp\u003eStudy I was run between September and October 2021; Study II was run on 20 October 2022. Before collecting data, we pre-registered our planned study design, hypotheses and statistical analyses on OSF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/5dm7h\u003c/span\u003e\u003cspan address=\"https://osf.io/5dm7h\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, date of pre-registration: September 20, 2021). Deviations from the pre-registered statistical analyses as well as exploratory analyses that were not pre-registered are clearly mentioned in the Results section.\u003c/p\u003e\u003cp\u003eEthics statement\u003c/p\u003e\u003cp\u003eThis research was performed in accordance with the Declaration of Helsinki, was performed in accordance with the relevant guidelines and regulations, and was\u003c/p\u003e\u003cp\u003eapproved by the Ethics Committee of the Medical Faculty of the University of Bonn (Approval Number Az. 419/21). Written informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eFor Study I, we recruited 240 American participants from Amazon MTurk, who completed the experiment through their web browsers. The number of participants was determined based on a power analysis in G*Power 3 \u003csup\u003e51\u003c/sup\u003e aiming to compare within-subject the means of the opinions before and after reading comments using a Wilcoxon signed-rank test (matched pairs), with a medium effect size of 0.24 (determined in a pilot study; see Supplementary Information), 0.05 alpha error probability, and 0.95 power, which prescribed a sample size of 228 participants. Participants were excluded if they moved through the survey at an unrealistic pace (e.g. if they took less than 3 seconds to read the comments); failed to properly answer the attention checks within the task; filled in the attitudinal questionnaire inconsistently (e.g. always giving the same answer even to reverse items). Easy attention checks were also included in the instructions, allowing the platform Qualtrics to automatically exclude participants who failed to correctly answer them. Based on these criteria, we excluded 46 datasets, leaving n = 194 participants (80 female; 9 between 18–25 years old, 33 between 26–30 years old; 91 between 31–40 years old, 61 above 40 years old) in the statistical analyses.\u003c/p\u003e\u003cp\u003eFor Study II, planned according to the same power analysis as Study I, we recruited 221 individuals (143 female; ໿147 between 18–25 years old; 53 between 26–30 years old; 21 above 31 years old) through the database of the BonnEconLab (University of Bonn, Germany) for the same online experiment. Registration in this database is voluntary and the pool is mainly made of University of Bonn students and staff. Participants were at least 18 years old and fluent in the English language (approximately B2 in the Common European Framework of Reference). Only one dataset was excluded based on the same criteria as Study I.\u003c/p\u003e\u003cp\u003eExperimental procedure\u003c/p\u003e\u003cp\u003eStudies 1 and 2 shared the same study design and experimental procedure, except that in Study II, we additionally collected answers to the DIMI questionnaire \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Participants started the experiment by reading the instructions which explained the purpose of the study and the exclusion criteria. After reading the instructions, participants completed validated attitudinal questionnaires about the three contemporary topics used in the task (see Pre-Existing Attitudes, below). Each participant was then presented with 3 out of 9 possible news headlines – one headline per topic (see Opinions about news headlines below, and see Supplementary Information for all headlines, comments and task development). Participants gave their opinions about the headlines before and after reading the comments. In Study II, participants then completed the DIMI questionnaire. At the end of the task, we gathered demographics data (sex and gender, age, education, political affiliations, Facebook and social media general usage). The study lasted between 20 and 25 minutes and participants were compensated with 6€ for their time.\u003c/p\u003e\u003cp\u003ePre-existing attitudes\u003c/p\u003e\u003cp\u003eParticipants completed three attitudinal questionnaires, about climate change \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, vaccination \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and veganism \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The scores obtained from these three questionnaires were used to classify participants as either supporting (if their score was above 50% of the maximum score) or opposing each topic. This classification was in turn used to determine which comments participants were be shown (see Comments, below). These scores were also z-scored and used as predictors of opinion change (see Statistical Analysis, below).\u003c/p\u003e\u003cp\u003eOpinions about news headlines\u003c/p\u003e\u003cp\u003eAfter completing the attitudinal questionnaires, participants were presented with a news headline without comments. The headlines were gathered from Facebook posts (see Supplementary Information). Participants then indicated their opinion about the content of the news headline using a slider ranging from − 7 (= strongly oppose) to + 7 (= strongly support). This rating was coded as R\u003csub\u003e1\u003c/sub\u003e, indicating participants’ “prior opinion” (prior to comments) about the headline. Participants then rated their confidence in this opinion on a scale from 0 (= not confident at all) to 100 (= absolutely confident). This first confidence rating was coded as C\u003csub\u003e1\u003c/sub\u003e. After this, four comments appeared below the same news headline (see Comments, below). Participants were instructed to carefully read these comments and then give their opinion and confidence a second time (coded as R\u003csub\u003e2\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e respectively) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This procedure was repeated for the other two news headlines. The order in which the topics were presented was randomised.\u003c/p\u003e\u003cp\u003eComments\u003c/p\u003e\u003cp\u003eComments (also gathered from Facebook, see Supplementary Information) presented after each headline were of three types: four comments supportive of the headline, four opposing comments, or two supportive and two opposing comments. Whether participants were exposed to supportive opposing comments on a given headline depended on their pre-existing attitude towards the topics of the headline. Each participant was confronted once with each of: i) four comments congruent with their initial attitude towards the topic of the headline, ii) four comments incongruent with their initial attitude, and iii) two congruent comments and two incongruent comments. This was done to assess in each participant (i.e., within-subject) the impact of comment congruence on opinion adjustment and confidence in opinions, and was implemented in Qualtrics using partial randomization. The type of comments was the primary experimental within-subject manipulation. While we thus determined which comments were shown after each news headline before exposing participants to the headlines, after acquiring the data we classified each trial as congruent, incongruent or control based on the congruence between the participant’s prior opinion (R\u003csub\u003e1\u003c/sub\u003e) and the comments shown (see “Effects of congruence …” section below).\u003c/p\u003e\u003cp\u003eStatistical analyses\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted with R language (version 4.3.2) and RStudio (version 2023.12.1.402, Posit Team, 2024), as well as the software JASP (version 0.18.2, JASP Team, 2023) for correlation matrices. The R packages employed for data cleaning, analysis and visualization included: \u003cem\u003eBayesFactor, cowplot, ggpubr, gmodels, Hmisc, kableExtra, lme4, nlme, plotrix, readxl, reshape2, see, tidyverse.\u003c/em\u003e All statistical analyses were conducted using a two-tailed test and a significance threshold of ໿α ≤ 0.05. All analyses described below were applied to the data of Study I and 2 except where indicated.\u003c/p\u003e\u003cp\u003eDeviation from the pre-registration\u003c/p\u003e\u003cp\u003eAs stated in the pre-registration of Study I, we planned to solely employ one-way ANOVA tests to assess our main hypotheses. However, for both studies, we ultimately opted to run mixed-effects linear model analyses, due to the non-normal distribution of our data and because of the robustness of these models against assumption violations. This was a more detailed and flexible approach to modelling the relationship between dependent and independent variables, because it allowed analysing single-trial data and including both fixed and random effects, resulting in a more accurate and comprehensive interpretation of the data. Therefore, these models were considered most suitable to address our research objectives. Furthermore, to accommodate control trials in the congruence analysis (see below), we deviated from the pre-registration and considered the congruence index as an ordinal variable with three instead of two levels: \u003cem\u003econgruent\u003c/em\u003e, \u003cem\u003econtrol\u003c/em\u003e and \u003cem\u003eincongruent\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eEffects of comment valence\u003c/p\u003e\u003cp\u003eTo determine the presence of a significant difference in opinion ratings before and after presenting the comments, as well as to assess the effectiveness of presenting mixed comments as a control condition, we performed three paired Wilcoxon tests (one for each level of the explanatory variable: supportive, opposing, mixed) between the first and the second opinion ratings. Subsequently, to examine whether the valence of the comments could predict the direction of the opinion shifts, opinion adjustments were computed (i.e., 𝑅\u003csub\u003e2\u003c/sub\u003e−𝑅\u003csub\u003e1\u003c/sub\u003e) and a mixed-effects linear regression model was performed using the function \u003cem\u003elme\u003c/em\u003e from the R package \u003cem\u003elme4\u003c/em\u003e. In this model, opinion adjustment (𝑅\u003csub\u003e2\u003c/sub\u003e−𝑅\u003csub\u003e1\u003c/sub\u003e) was the dependent variable, the valence of the comments (i.e. supportive, opposing, mixed) was the explanatory variable, and random intercepts were included to account for individual differences in the average opinion ratings:\u003c/p\u003e\u003cp\u003e\u003cb\u003e(Eq.\u0026nbsp;1).\u003c/b\u003e \u003cem\u003eOpinion adjustment\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​ + β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eComment valence\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ u\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ ϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eThe subscript \u003cem\u003ej\u003c/em\u003e indexes participants, and the subscript \u003cem\u003ei\u003c/em\u003e indexes observations. \u003cem\u003eOpinion adjustment\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the dependent variable and \u003cem\u003eComment valence\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the independent variable. \u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e indicates participant-specific random intercepts and \u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e the residual.\u003c/p\u003e\u003cp\u003eEffects of congruence between initial opinion and comment valence on opinion adjustment\u003c/p\u003e\u003cp\u003eWe aimed to evaluate the effect of the congruence between the participant’s initial opinion and the opinion expressed in the comments on opinion adjustment. Recall that initial opinion (R\u003csub\u003e1\u003c/sub\u003e) was a number between − 7 and 7. Trials were labelled as “\u003cem\u003econgruent condition\u003c/em\u003e” either when participants’ first opinion was supportive (R\u003csub\u003e1\u003c/sub\u003e \u0026gt; 0) and the comments’ valence were also supportive, or when participants’ first opinion was opposing (R\u003csub\u003e1\u003c/sub\u003e \u0026lt; 0) and comments were also opposing. Trials were labelled as “\u003cem\u003eincongruent condition\u003c/em\u003e” either when participants’ first opinion was supportive (R\u003csub\u003e1\u003c/sub\u003e \u0026gt; 0) and the comments’ valence were opposing, or when participants’ first opinion was opposing (R\u003csub\u003e1\u003c/sub\u003e \u0026lt; 0) and comments were supportive. Trials in which comments were mixed were labelled as “\u003cem\u003econtrol condition\u003c/em\u003e”. Trials in which R\u003csub\u003e1\u003c/sub\u003e was exactly 0 (this was the case in 12 of 582 = 2% of trials in Study 1, and in 7 of 663 = 1.1% of trials in Study 2) were excluded from the analysis. To investigate the influence of congruence on opinion adjustment, we ran a mixed-effects linear regression model. The dependent variable was the absolute value of opinion adjustment (i.e., |𝑅\u003csub\u003e2\u003c/sub\u003e−𝑅\u003csub\u003e1\u003c/sub\u003e|), and congruence was an explanatory ordinal variable with three levels (\u003cem\u003econgruent\u003c/em\u003e, \u003cem\u003econtrol\u003c/em\u003e or \u003cem\u003eincongruent\u003c/em\u003e). Random intercepts were included to account for individual differences in the average opinion ratings:\u003c/p\u003e\u003cp\u003e\u003cb\u003e(Eq.\u0026nbsp;2).\u003c/b\u003e \u003cem\u003eOpinion adjustment\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​ + β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCongruence\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ u\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ ϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eEffects of congruence between initial opinion and comment valence on confidence adjustment\u003c/p\u003e\u003cp\u003eWe assessed the effect of the congruence between first opinion and comments on participants’ adjustment in confidence in their opinion. Confidence adjustments were computed as the difference between confidence in the second and the first opinions (i.e. C\u003csub\u003e2\u003c/sub\u003e − C\u003csub\u003e1\u003c/sub\u003e). We ran a mixed-effects linear model with confidence adjustment as dependent variable and congruence as the independent ordinal variable, with three levels: \u003cem\u003econgruent\u003c/em\u003e, \u003cem\u003econtrol\u003c/em\u003e, \u003cem\u003eincongruent\u003c/em\u003e. We used random intercepts to control for individual differences in the average confidence ratings:\u003c/p\u003e\u003cp\u003e\u003cb\u003e(Eq.\u0026nbsp;3).\u003c/b\u003e \u003cem\u003eConfidence adjustment\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​ + β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCongruence\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ u\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ ϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eEffects of pre-existing attitudes towards headline topics on opinion adjustment\u003c/p\u003e\u003cp\u003eTo investigate the relationship between pre-existing attitudes towards the topics of the headlines and opinion adjustment, we performed a linear regression with pre-existing attitude as the explanatory variable and opinion adjustment (|𝑅\u003csub\u003e2\u003c/sub\u003e−𝑅\u003csub\u003e1\u003c/sub\u003e|) as dependent variable. Due to the variations in the number of items and the different Likert scales across the three questionnaires, the resulting scores were standardised into z-scores to obtain a unique index of pre-existing attitudes that ranged from 0 (weak attitude towards the topic) to 50 (strong attitude towards the topic). The equation was:\u003c/p\u003e\u003cp\u003e\u003cb\u003e(Eq.\u0026nbsp;4).\u003c/b\u003e \u003cem\u003eOpinion adjustment\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​ + β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAttitude score\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ ϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eEffects of digital maturity on opinion adjustment (Study II)\u003c/p\u003e\u003cp\u003eTo assess whether more digital mature individuals were less susceptible to the influence of others’ opinions, we ran correlation analyses between opinion adjustment (|𝑅\u003csub\u003e2\u003c/sub\u003e−𝑅\u003csub\u003e1\u003c/sub\u003e|) and the DIMI \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e with its three sub-categories: The \u003cem\u003ecapacity to use digital technologies in an autonomous and self-determined way\u003c/em\u003e; the \u003cem\u003ecapacity to master increasing digital challenges and solve problems\u003c/em\u003e; and the \u003cem\u003ecapacity to interact adequately with others and to contribute to society\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eAssessing data sensitivity with Bayesian hypothesis testing\u003c/p\u003e\u003cp\u003eTo clarify whether non-significant results support hull hypothesis or simply reflect data insensitivity, we additionally calculated Bayes Factors (BF) to assess the level of evidence for the alternative hypothesis over the null hypothesis. We used default priors in the R package \u003cem\u003eBayesFactor\u003c/em\u003e and the often-used convention attributed to Harold Jeffreys (see Dienes 2014): a BF greater than 3 indicated that there was significant evidence for H1 over H0, while a BF less than 1/3 indicated that there was significant evidence for H0 over H1. A BF between 1/3 and 3 indicated that the data was insensitive to distinguish between H1 and H0.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData and code availability\u003c/h2\u003e\n\u003cp\u003eThe stimuli, data and code for this study are openly available at https://osf.io/sv5wn/. The preregistration is at https://osf.io/5dm7h.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was funded by the European Union\u0026rsquo;s Horizon 2020 research and innovation programme [grant number 870578].\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eF.N.\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing. \u003cstrong\u003eJ.W.\u003c/strong\u003e: Data curation, Methodology, Resources, Writing \u0026ndash; review and editing. \u003cstrong\u003eS.L.\u003c/strong\u003e: Conceptualization, Writing \u0026ndash; review and editing. \u003cstrong\u003eD.S.\u003c/strong\u003e: Writing \u0026ndash; review and editing. \u003cstrong\u003eW.B.\u003c/strong\u003e: Conceptualization, Methodology, Writing \u0026ndash; review and editing. \u003cstrong\u003eJ.S.\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003ch2\u003eAdditional Information\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJordan, T. \u003cem\u003eInternet, Society and Culture\u003c/em\u003e. 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Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 6840 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7876614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7876614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the current age many people encounter news through social media. This mode of information acquisition is characterised by the presence of reader\u0026rsquo;s comments published together with the news. Previous studies have shown that these comments influence people\u0026rsquo;s opinion about the news, which raises questions about the vulnerability of public opinions. We developed an ecological task to replicate and investigate this phenomenon experimentally. In our task, participants were presented with headlines on important contemporary issues actually posted on Facebook, together with actual readers\u0026rsquo; comments, in a display replicating the social media platform. Participants in two pre-registered studies run in different countries (USA, N\u0026thinsp;=\u0026thinsp;220; Germany, N\u0026thinsp;=\u0026thinsp;220) consistently adjusted their opinions in accordance with the sentiment expressed in the comments. The degree of opinion change was significantly greater when participants had weak pre-existing attitudes toward the topics and when they had low confidence in their initial opinions about the news. An exploratory analysis revealed greater susceptibility to social influence in less digitally mature participants. This study replicates under controlled conditions the powerful influence of online social media comments in shaping public opinion and points to some variables moderating this influence.\u003c/p\u003e","manuscriptTitle":"Influence of social media comments on opinions about news","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:30:06","doi":"10.21203/rs.3.rs-7876614/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"4024151257510144802775149599134069772","date":"2026-05-12T13:57:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20328655167215659574354686212031851030","date":"2026-05-03T08:27:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T14:12:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178142488631275170555950630596402255243","date":"2025-11-03T08:35:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T15:28:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-22T13:55:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-22T13:51:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-20T10:14:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-20T10:07:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5af6dab8-3ad1-4614-9c7f-06f541e57cd6","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"4024151257510144802775149599134069772","date":"2026-05-12T13:57:19+00:00","index":123,"fulltext":""},{"type":"reviewerAgreed","content":"20328655167215659574354686212031851030","date":"2026-05-03T08:27:22+00:00","index":109,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57429788,"name":"Humanities/Cultural and media studies"},{"id":57429789,"name":"Social science/Cultural and media studies"},{"id":57429790,"name":"Biological sciences/Psychology"},{"id":57429791,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-11-11T16:30:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 16:30:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7876614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7876614","identity":"rs-7876614","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}