False consensus in the echo chamber: Exposure to favorably biased social media news feeds leads to increased perception of public support for own opinions

Abstract

Studies suggest that users of online social networking sites can tend to preferably connect with like-minded others, leading to “Echo Chambers” in which attitudinally congruent information circulates. However, little is known about how exposure to artifacts of Echo Chambers, such as biased attitudinally congruent online news feeds, affects individuals’ perceptions and behavior. This study experimentally tested if exposure to attitudinally congruent online news feeds affects individuals' False Consensus Effect, that is, how strongly individuals perceive public opinions as favorably biased and in support of their own opinions. It was predicted that the extent of the False Consensus Effect is influenced by the level of agreement individuals encounter in online news feeds, with high agreement leading to a higher estimate of public support for their own opinions than low agreement. Two online experiments (n1 = 331 and n2 = 207) exposed participants to nine news feeds, each containing four messages. Two factors were manipulated: Agreement expressed in message texts (all but one [Exp.1] / all [Exp.2] messages were congruent or incongruent to participants' attitudes) and endorsement of congruent messages by other users (congruent messages displayed higher or lower numbers of “likes” than incongruent messages). Additionally, based on Elaboration Likelihood Theory, interest in a topic was considered as a moderating variable. Both studies confirmed that participants infer public support for their own attitudes from the degree of agreement they encounter in online messages, yet are skeptical of the validity of “likes”, especially if their interest in a topic is high.

Bibliographic citation

Luzsa, R., & Mayr, S. (2021). False consensus in the echo chamber: Exposure to favorably biased social media news feeds leads to increased perception of public support for own opinions. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 15(1), Article 3. doi:https://doi.org/10.5817/CP2021-1-3

Keywords

Echo chambers; social networking; false consensus; selective exposure

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Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888–918. https://doi.org/10.1037/0033-2909.84.5.888

Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211

Asch, S. E. (1961). Effects of group pressure upon the modification and distortion of judgments. In M. Henle (Ed.), Documents of gestalt psychology (pp. 222–236). University of California Press. https://doi.org/10.1525/9780520313514-017

Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005

Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. https://doi.org/10.1177/0956797615594620

Bastos, M. T. (2015). Shares, pins, and tweets: News readership from daily papers to social media. Journalism Studies, 16(3), 305–325. https://doi.org/10.1080/1461670X.2014.891857

Bauman, K. P., & Geher, G. (2002). We think you agree: The detrimental impact of the false consensus effect on behavior. Current Psychology, 21(4), 293–318. https://doi.org/10.1007/s12144-002-1020-0

BDP, & DGPs. (2016). Berufsethische Richtlinien des Berufsverbands Deutscher Psychologinnen und Psychologen und der Deutschen Gesellschaft für Psychologie [Professional Ethical Guidelines of the Professional Association of German Psychologists e.V. and the German Psychological Society e.V.]. https://www.dgps.de/fileadmin/documents/Empfehlungen/ber-foederation-2016.pdf

Beam, M. A. (2014). Automating the news: How personalized news recommender system design choices impact news reception. Communication Research, 41(8), 1019–1041. https://doi.org/10.1177/0093650213497979

Berlyne, D. E., & Ditkofksy, J. (1976). Effects of novelty and oddity on visual selective attention. British Journal of Psychology, 67(2), 175–180. https://doi.org/10.1111/j.2044-8295.1976.tb01508.x

Bruns, A. (2017, September 14). Echo chamber? What echo chamber? Reviewing the evidence [Poster presentation]. 6th Biennial Future of Journalism Conference (FOJ17), Cardiff. https://eprints.qut.edu.au/113937/

Chang, Y.-T., Yu, H., & Lu, H.-P. (2015). Persuasive messages, popularity cohesion, and message diffusion in social media marketing. Journal of Business Research, 68(4), 777–782. https://doi.org/10.1016/j.jbusres.2014.11.027

Cinelli, M., Brugnoli, E., Schmidt, A. L., Zollo, F., Quattrociocchi, W., & Scala, A. (2020). Selective exposure shapes the Facebook news diet. PLoS ONE, 15(3), Article e0229129. https://doi.org/10.1371/journal.pone.0229129

Çoklar, A. N., Yaman, N. D., & Yurdakul, I. K. (2017). Information literacy and digital nativity as determinants of online information search strategies. Computers in Human Behavior, 70, 1–9. https://doi.org/10.1016/j.chb.2016.12.050

Cotton, J. L., & Hieser, R. A. (1980). Selective exposure to information and cognitive dissonance. Journal of Research in Personality, 14(4), 518–527. https://doi.org/10.1016/0092-6566(80)90009-4

de la Haye, A.-M. (2000). A methodological note about the measurement of the false-consensus effect. European Journal of Social Psychology, 30(4), 569–581. https://doi.org/10.1002/1099-0992(200007/08)30:4<569::AID-EJSP8>3.0.CO;2-V

Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016). The spreading of misinformation online. PNAS: Proceedings of the National Academy of Sciences of the United States of America, 113(3), 554–559. https://doi.org/10.1073/pnas.1517441113

Dubois, E., & Blank, G. (2018). The echo chamber is overstated: The moderating effect of political interest and diverse media. Information, Communication & Society, 21(5), 729–745. https://doi.org/10.1080/1369118X.2018.1428656

Duggan, M., & Smith, A. (2016). The political environment on social media. Pew Research Center. https://www.pewresearch.org/internet/2016/10/25/the-political-environment-on-social-media/

Dvir-Gvirsman, S. (2019). I like what I see: Studying the influence of popularity cues on attention allocation and news selection. Information, Communication & Society, 22(2), 286–305. https://doi.org/10.1080/1369118X.2017.1379550

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

Fishbein, M. (1976). A behavior theory approach to the relations between beliefs about an object and the attitude toward the object. In U. H. Funke (Ed.), Mathematical models in marketing: A collection of abstracts (pp. 87–88). Springer. https://doi.org/10.1007/978-3-642-51565-1_25

Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. The Journal of Socio-Economics, 40(1), 35–42. https://doi.org/10.1016/j.socec.2010.10.008

Galesic, M., Olsson, H., & Rieskamp, J. (2013). False consensus about false consensus. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 472–476). http://csjarchive.cogsci.rpi.edu/Proceedings/2013/papers/0109/paper0109.pdf

Giese, H., Neth, H., Moussaïd, M., Betsch, C., & Gaissmaier, W. (2020). The echo in flu-vaccination echo chambers: Selective attention trumps social influence. Vaccine, 38(8), 2070–2076. https://doi.org/10.1016/j.vaccine.2019.11.038

Gilbert, E., Bergstrom, T., & Karahalios, K. (2009). Blogs are echo chambers: Blogs are echo chambers. In Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS’09). IEEE. https://doi.org/10.1109/HICSS.2009.91

Grömping, M. (2014). ‘Echo chambers’: Partisan Facebook groups during the 2014 Thai election. Asia Pacific Media Educator, 24(1), 39–59. https://doi.org/10.1177/1326365X14539185

Guess, A., Nagler, J., & Tucker, J. (2019). Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances, 5(1), Article eaau4586. https://doi.org/10.1126/sciadv.aau4586

Guest, E. (2018). (Anti-)echo chamber participation: Examining contributor activity beyond the chamber. In SMSociety ’18: Proceedings of the 9th International Conference on Social Media and Society (pp. 301–304). ACM. https://doi.org/10.1145/3217804.3217933

Guo, L., A. Rohde, J., & Wu, H. D. (2020). Who is responsible for Twitter’s echo chamber problem? Evidence from 2016 U.S. election networks. Information, Communication & Society, 23(2), 234–251. https://doi.org/10.1080/1369118X.2018.1499793

Haim, M., Kümpel, A. S., & Brosius, H.-B. (2018). Popularity cues in online media: A review of conceptualizations, operationalizations, and general effects. SCM: Studies in Communication and Media, 7(2), 186–207. https://doi.org/10.5771/2192-4007-2018-2-58

Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135(4), 555–588. https://doi.org/10.1037/a0015701

Kim, A., & Dennis, A. R. (2019). Says who? The effects of presentation format and source rating on fake news in social media. MIS Quarterly, 43(3), 1025–1039. https://doi.org/10.25300/MISQ/2019/15188

Knobloch-Westerwick, S. (2014). Choice and preference in media use: Advances in selective exposure theory and research. Routledge. https://doi.org/10.4324/9781315771359

Knobloch-Westerwick, S. & Meng, J. (2009). Looking the other way: Selective exposure to attitude-consistent and counterattitudinal political information. Communication Research, 36(3), 426–448. https://doi.org/10.1177/0093650209333030

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13). https://doi.org/10.18637/jss.v082.i13

Lee, E.-J. & Jang, Y. J. (2010). What do others’ reactions to news on Internet portal sites tell us? Effects of presentation format and readers’ need for cognition on reality perception. Communication Research, 37(6), 825–846. https://doi.org/10.1177/0093650210376189

Liska, A. E. (1984). A critical examination of the causal structure of the Fishbein/Ajzen attitude-behavior model. Social Psychology Quarterly, 47(1), 61–74. https://doi.org/10.2307/3033889

Luzsa, R. & Mayr, S. (2019). Links between users' online social network homogeneity, ambiguity tolerance, and estimated public support for own opinions. Cyberpsychology, Behavior and Social Networking, 22(5), 325-329. https://doi.org/10.1089/cyber.2018.0550

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Messing, S., & Westwood, S. J. (2014). Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online. Communication Research, 41(8), 1042–1063. https://doi.org/10.1177/0093650212466406

Nguyen, A., & Vu, H. T. (2019). Testing popular news discourse on the “echo chamber” effect: Does political polarisation occur among those relying on social media as their primary politics news source? First Monday, 24(6). https://doi.org/10.5210/fm.v24i6.9632

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Asch, S. E. (1961). Effects of group pressure upon the modification and distortion of judgments. In M. Henle (Ed.), Documents of gestalt psychology (pp. 222–236). University of California Press.

Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005

Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. https://doi.org/10.1177/0956797615594620

Bastos, M. T. (2015). Shares, pins, and tweets: News readership from daily papers to social media. Journalism Studies, 16(3), 305–325. https://doi.org/10.1080/1461670X.2014.891857

Bauman, K. P., & Geher, G. (2002). We think you agree: The detrimental impact of the false consensus effect on behavior. Current Psychology, 21(4), 293–318. https://doi.org/10.1007/s12144-002-1020-0

BDP, & DGPs. (2016). Berufsethische Richtlinien des Berufsverbands Deutscher Psychologinnen und Psychologen und der Deutschen Gesellschaft für Psychologie [Professional Ethical Guidelines of the Professional Association of German Psychologists e.V. and the German Psychological Society e.V.]. https://www.dgps.de/fileadmin/documents/Empfehlungen/ber-foederation-2016.pdf

Beam, M. A. (2014). Automating the news: How personalized news recommender system design choices impact news reception. Communication Research, 41(8), 1019–1041. https://doi.org/10.1177/0093650213497979

Berlyne, D. E., & Ditkofksy, J. (1976). Effects of novelty and oddity on visual selective attention. British Journal of Psychology, 67(2), 175–180. https://doi.org/10.1111/j.2044-8295.1976.tb01508.x

Bruns, A. (2017, September 14). Echo chamber? What echo chamber? Reviewing the evidence [Poster presentation]. 6th Biennial Future of Journalism Conference (FOJ17), Cardiff. https://eprints.qut.edu.au/113937/

Chang, Y.-T., Yu, H., & Lu, H.-P. (2015). Persuasive messages, popularity cohesion, and message diffusion in social media marketing. Journal of Business Research, 68(4), 777–782. https://doi.org/10.1016/j.jbusres.2014.11.027

Cinelli, M., Brugnoli, E., Schmidt, A. L., Zollo, F., Quattrociocchi, W., & Scala, A. (2020). Selective exposure shapes the Facebook news diet. PLoS ONE, 15(3), Article e0229129. https://doi.org/10.1371/journal.pone.0229129

Çoklar, A. N., Yaman, N. D., & Yurdakul, I. K. (2017). Information literacy and digital nativity as determinants of online information search strategies. Computers in Human Behavior, 70, 1–9. https://doi.org/10.1016/j.chb.2016.12.050

Cotton, J. L., & Hieser, R. A. (1980). Selective exposure to information and cognitive dissonance. Journal of Research in Personality, 14(4), 518–527. https://doi.org/10.1016/0092-6566(80)90009-4

de la Haye, A.-M. (2000). A methodological note about the measurement of the false-consensus effect. European Journal of Social Psychology, 30(4), 569–581. https://doi.org/10.1002/1099-0992(200007/08)30:4<569::AID-EJSP8>3.0.CO;2-V

Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016). The spreading of misinformation online. PNAS: Proceedings of the National Academy of Sciences of the United States of America, 113(3), 554–559. https://doi.org/10.1073/pnas.1517441113

Dubois, E., & Blank, G. (2018). The echo chamber is overstated: The moderating effect of political interest and diverse media. Information, Communication & Society, 21(5), 729–745. https://doi.org/10.1080/1369118X.2018.1428656

Duggan, M., & Smith, A. (2016). The political environment on social media. Pew Research Center. https://www.pewresearch.org/internet/2016/10/25/the-political-environment-on-social-media/

Dvir-Gvirsman, S. (2019). I like what I see: Studying the influence of popularity cues on attention allocation and news selection. Information, Communication & Society, 22(2), 286–305. https://doi.org/10.1080/1369118X.2017.1379550

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

Fishbein, M. (1976). A behavior theory approach to the relations between beliefs about an object and the attitude toward the object. In U. H. Funke (Ed.), Mathematical models in marketing: A collection of abstracts (pp. 87–88). Springer. https://doi.org/10.1007/978-3-642-51565-1_25

Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. The Journal of Socio-Economics, 40(1), 35–42. https://doi.org/10.1016/j.socec.2010.10.008

Galesic, M., Olsson, H., & Rieskamp, J. (2013). False consensus about false consensus. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 472–476). http://csjarchive.cogsci.rpi.edu/Proceedings/2013/papers/0109/paper0109.pdf

Giese, H., Neth, H., Moussaïd, M., Betsch, C., & Gaissmaier, W. (2020). The echo in flu-vaccination echo chambers: Selective attention trumps social influence. Vaccine, 38(8), 2070–2076. https://doi.org/10.1016/j.vaccine.2019.11.038

Gilbert, E., Bergstrom, T., & Karahalios, K. (2009). Blogs are echo chambers: Blogs are echo chambers. In Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS’09). IEEE. https://doi.org/10.1109/HICSS.2009.91

Grömping, M. (2014). ‘Echo chambers’: Partisan Facebook groups during the 2014 Thai election. Asia Pacific Media Educator, 24(1), 39–59. https://doi.org/10.1177/1326365X14539185

Guess, A., Nagler, J., & Tucker, J. (2019). Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances, 5(1), Article eaau4586. https://doi.org/10.1126/sciadv.aau4586

Guest, E. (2018). (Anti-)echo chamber participation: Examining contributor activity beyond the chamber. In SMSociety ’18: Proceedings of the 9th International Conference on Social Media and Society (pp. 301–304). ACM. https://doi.org/10.1145/3217804.3217933

Guo, L., A. Rohde, J., & Wu, H. D. (2020). Who is responsible for Twitter’s echo chamber problem? Evidence from 2016 U.S. election networks. Information, Communication & Society, 23(2), 234–251. https://doi.org/10.1080/1369118X.2018.1499793

Haim, M., Kümpel, A. S., & Brosius, H.-B. (2018). Popularity cues in online media: A review of conceptualizations, operationalizations, and general effects. SCM: Studies in Communication and Media, 7(2), 186–207. https://doi.org/10.5771/2192-4007-2018-2-58

Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135(4), 555–588. https://doi.org/10.1037/a0015701

Kim, A., & Dennis, A. R. (2019). Says who? The effects of presentation format and source rating on fake news in social media. MIS Quarterly, 43(3), 1025–1039. https://doi.org/10.25300/MISQ/2019/15188

Knobloch-Westerwick, S. (2014). Choice and preference in media use: Advances in selective exposure theory and research. Routledge.

Knobloch-Westerwick, S. & Meng, J. (2009). Looking the other way: Selective exposure to attitude-consistent and counterattitudinal political information. Communication Research, 36(3), 426–448. https://doi.org/10.1177/0093650209333030

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13). https://doi.org/10.18637/jss.v082.i13

Lee, E.-J. & Jang, Y. J. (2010). What do others’ reactions to news on Internet portal sites tell us? Effects of presentation format and readers’ need for cognition on reality perception. Communication Research, 37(6), 825–846. https://doi.org/10.1177/0093650210376189

Liska, A. E. (1984). A critical examination of the causal structure of the Fishbein/Ajzen attitude-behavior model. Social Psychology Quarterly, 47(1), 61–74. https://doi.org/10.2307/3033889

Luzsa, R. & Mayr, S. (2019). Links between users' online social network homogeneity, ambiguity tolerance, and estimated public support for own opinions. Cyberpsychology, Behavior and Social Networking, 22(5), 325-329. https://doi.org/10.1089/cyber.2018.0550

McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444. https://doi.org/10.1146/annurev.soc.27.1.415

Messing, S., & Westwood, S. J. (2014). Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online. Communication Research, 41(8), 1042–1063. https://doi.org/10.1177/0093650212466406

Nguyen, A., & Vu, H. T. (2019). Testing popular news discourse on the “echo chamber” effect: Does political polarisation occur among those relying on social media as their primary politics news source? First Monday, 24(6). https://doi.org/10.5210/fm.v24i6.9632

Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin.

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https://doi.org/10.5817/CP2021-1-3