過往跨立場接觸文獻鮮少探討介面設計的應用。為填補此缺口，本研究旨在探討使用者在社群平台的跨立場接觸情境下，面對意見分歧時的完整認知與情感路徑，及其對平台參與意願的影響；同時結合過往文獻提到之朋友這一身份，在減緩立場對立的潛力。本研究提出推送平衡立場之釘選機制作為介面設計的推力，結合朋友留言的社會線索，實際透過架設模擬臺灣臉書的擬真網站—Weshare，並藉由想像接觸提示引導來自朋友的社會線索進入網站。針對臺灣社會既存之爭議性議題，以94名有效樣本進行2×2線上隨機實驗，執行單因子變異數分析、結構方程模型與階層迴歸分析。分析結果驗證釘選機制顯著降低使用者在平台上感知到的資訊過載，顯示此機制有效聚焦平台使用者注意力於貼文留言區頂部，但卻進一步推升情感態度極端性；使用者對留言區的感知意見氛圍則正向影響認知態度極端性，顯示一致的意見氛圍會提供外部驗證並強化既有其既有信念與態度。在平台參與度部分，在情感層面上的情感態度極端性會顯著正向驅動互動與迴避意願，反映留言區之高張力情緒一方面促成按讚與留言表態的意願，另一方面也引發情感防禦性的迴避意願；認知態度極端性則負向影響迴避意願，即代表立場態度堅定者，更傾向主動捍衛自身觀點而非逃避。最後，雖然有發現留言類型的調節作用，卻未發現朋友留言顯著削弱資訊過載對態度極端性的影響。總結而言，釘選機制作為介面干預機制，雖能有效降低資訊過載，卻可能因放大不同意見群體之間分歧，更突顯群體間差異而成為激化情感防禦的雙面刃。結果揭露了殘酷的真相：「緩解極端態度以築起共識的橋樑需要傾盡心力，而點燃情感對立的野火，卻僅在彈指之間。」。建議當今社群平台在設計能讓平台使用者接觸跨立場的多元觀點之介面時，不應僅是並列分歧立場的意見，須謹慎搭配情感引導提示或軟性審查釘選內容的機制，方能促進不同立場群體間的理性數位交流，產生不排斥互動也不刻意迴避其他意見群體的正面參與平台意願。

The study examines how interventions in the social media interface affect users' cognitive and emotional responses, as well as their intentions to participate in cross-cutting discussions. To this end, a pinning nudge (pinning mechanism) is proposed that shows balanced comments on the stance at the top of discussion threads, together with social cues from friends. An individually developed platform, "Weshare", which mimics the interface of Taiwan's Facebook, was used to carry out an online 2-by-2 randomized experiment (N = 94) on three contentious political topics. The data were analyzed using ANOVA, PLS-SEM, and hierarchical regression. The findings show that the pinning nudge significantly reduces perceived information overload by directing users' visual attention to the top comments. However, while it lightens this cognitive load, it also heightens affective attitude extremity. A positive effect of the perceived opinion climate on cognitive attitude extremity indicates that agreeing with the majority opinion strengthens one's current beliefs. Moreover, affective attitude extremity positively impacts both interaction and avoidance intentions, showing that emotional arousal leads to active engagement (e.g., likes or comments) while also causing defensive withdrawal.
On the other hand, cognitive attitude extremity influences avoidance intention negatively, meaning that people with strongly held rational beliefs tend to prefer defending their views rather than withdrawing. Friend comments did not have a significant moderating effect in reducing attitude extremity. In summary, although the pinning nudge is effective at reducing cognitive load, it can be a double-edged sword since it leads to group polarization and provokes affective defenses. Therefore, platform designers should combine pinning nudge with civility prompts to promote constructive digital discussion.

蕭怡靖. (2014). 從政黨情感溫度計解析台灣民眾的政治極化 [Political Polarization in Taiwan: An Analysis on Mass Feeling Thermometer toward Political Parties]. 選舉研究, 21(2), 1–42. https://doi.org/10.6612/tjes.2014.21.02.01-42
蕭怡靖. (2019). 台灣民眾的黨性極化及其對民主態度的影響The People＇s Partisan Polarization and Its Influence on the Attitudes toward Democracy in Taiwan. Vol.23(No.2), 41–85. Retrieved 2019–12, from http://nccur.lib.nccu.edu.tw/handle/140.119/147706
Agresti, A. (2013). Categorical data analysis. John Wiley & Sons.
Ahn, S., & Carnahan, D. (2025). How Does Emphasizing Bipartisan Agreement Reduce Affective Polarization? Examining Three Dimensions of Perceived Similarity as Mediators. Journal of Broadcasting & Electronic Media, 69(3), 254–272. https://doi.org/10.1080/08838151.2025.2492868
Aiken, L. S. (1991). Multiple regression: Testing and interpreting interactions. In: sage Newbury Park, CA.
Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020). The welfare effects of social media. American economic review, 110(3), 629–676.
Arnold, M., Goldschmitt, M., & Rigotti, T. (2023). Dealing with information overload: a comprehensive review [Review]. Frontiers in Psychology, Volume 14 - 2023. https://doi.org/10.3389/fpsyg.2023.1122200
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In Psychology of learning and motivation (Vol. 2, pp. 89–195). Elsevier.
Bai, X., Lian, S., Sun, X., Niu, G., & Liu, J. (2025). The relationship between information hoarding and selective exposure: the role of information overload, identity bubble reinforcement, and intolerance of uncertainty. BMC Psychol, 13(1), 736. https://doi.org/10.1186/s40359-025-03062-8
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the national academy of sciences, 115(37), 9216–9221.
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132.
Baldassarri, D., & Gelman, A. (2008). Partisans without constraint: Political polarization and trends in American public opinion. American journal of Sociology, 114(2), 408–446.
Balietti, S., Getoor, L., Goldstein, D. G., & Watts, D. J. (2021). Reducing opinion polarization: Effects of exposure to similar people with differing political views. Proc Natl Acad Sci U S A, 118(52). https://doi.org/10.1073/pnas.2112552118
Barberá, P. (2014). How Social Media Reduces Mass Political Polarization. Evidence from Germany, Spain, and the U.S.
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of general psychology, 5(4), 323–370.
Bawden, D., & Robinson, L. (2020). Information overload: An overview.
Bennett, W. L., & Iyengar, S. (2008). A new era of minimal effects? The changing foundations of political communication. Journal of Communication, 58(4), 707–731.
Boot, A. B., Dijkstra, K., & Zwaan, R. A. (2021). The processing and evaluation of news content on social media is influenced by peer-user commentary. Humanities and Social Sciences Communications, 8(1), 209. https://doi.org/10.1057/s41599-021-00889-5
Breckler, S. J., & Wiggins, E. C. (1989). Affect versus evaluation in the structure of attitudes. Journal of experimental social psychology, 25(3), 253–271.
Brooks, D. J., & Geer, J. G. (2007). Beyond negativity: The effects of incivility on the electorate. American journal of political science, 51(1), 1–16.
Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of social media users: An S-O-R perspective. Computers in Human Behavior, 81, 10–18. https://doi.org/https://doi.org/10.1016/j.chb.2017.11.035
Cargnino, M., & Neubaum, G. (2024). Is it better to strike a balance? How exposure to congruent and incongruent opinion climates on social networking sites impacts users’ processing and selection of information. New Media & Society, 26(5), 2429–2447. https://doi.org/10.1177/14614448221083914
Chen, H.-T. (2018). Spiral of silence on social media and the moderating role of disagreement and publicness in the network: Analyzing expressive and withdrawal behaviors. New Media & Society, 20(10), 3917–3936. https://doi.org/10.1177/1461444818763384
Cohen, J. (1992). A power primer. Psychol Bull, 112(1), 155–159. https://doi.org/10.1037//0033-2909.112.1.155
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences, 3rd ed. Lawrence Erlbaum Associates Publishers.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
Combs, A., Tierney, G., Guay, B., Merhout, F., Bail, C. A., Hillygus, D. S., & Volfovsky, A. (2023). Reducing political polarization in the United States with a mobile chat platform. nature human behaviour, 7(9), 1454–1461.
Conner, M., & Norman, P. (2025). Attitudes, intentions, and behavior change. Annual Review of Psychology, 77.
Covington-Guillory, E. N., Madison, T. P., & Winters, C. (2024). A Thematic Analysis of Enhancing Constructive Political Conflict Using Positive Imagined Interactions. Imagination, Cognition and Personality, 43(3), 197–228. https://doi.org/10.1177/02762366231213997
Crisp, R. J., & Turner, R. N. (2009). Can imagined interactions produce positive perceptions? Reducing prejudice through simulated social contact. Am Psychol, 64(4), 231–240. https://doi.org/10.1037/a0014718
DiMaggio, P., Evans, J., & Bryson, B. (1996). Have American's social attitudes become more polarized? American journal of Sociology, 102(3), 690–755.
Dutceac Segesten, A., Bossetta, M., Holmberg, N., & Niehorster, D. (2022). The cueing power of comments on social media: how disagreement in Facebook comments affects user engagement with news. Information, Communication & Society, 25(8), 1115–1134. https://doi.org/10.1080/1369118X.2020.1850836
Egan, J., Robertson, C., Ross Arguedas, A., Newman, N., Nielsen, R., Mukherjee, M., & Fletcher, R. (2026). Reuters Institute digital news report 2026. 158–159.
Eveland, W. P., Jr., Coduto, K. D., Appiah, O., & Bullock, O. M. (2020). Listening During Political Conversations: Traits and Situations. Political Communication, 37(5), 656–677. https://doi.org/10.1080/10584609.2020.1736701
Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.
Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited.
Fiorina, M. P., & Abrams, S. J. (2008). Political Polarization in the American Public. Annual Review of Political Science, 11(Volume 11, 2008), 563–588. https://doi.org/https://doi.org/10.1146/annurev.polisci.11.053106.153836
Fischer, P., Greitemeyer, T., & Frey, D. (2008). Self-regulation and selective exposure: the impact of depleted self-regulation resources on confirmatory information processing. J Pers Soc Psychol, 94(3), 382–395. https://doi.org/10.1037/0022-3514.94.3.382
Flanagin, A. J., Hocevar, K. P., & Samahito, S. N. (2014). Connecting with the user-generated Web: how group identification impacts online information sharing and evaluation. Information, Communication & Society, 17(6), 683–694. https://doi.org/10.1080/1369118X.2013.808361
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error [doi:10.2307/3151312, American Marketing Association]. US.
Fossen, B. L., & Schweidel, D. A. (2026). Emotionality in political social media communications: The moderating role of audience diversity. INFORMATION SYSTEMS RESEARCH, 37(1), 434–453.
Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of counseling psychology, 51(1), 115.
Fu, S., Li, H., Liu, Y., Pirkkalainen, H., & Salo, M. (2020). Social media overload, exhaustion, and use discontinuance: Examining the effects of information overload, system feature overload, and social overload. Information Processing & Management, 57(6), 102307. https://doi.org/https://doi.org/10.1016/j.ipm.2020.102307
Gao, M., Xiao, Z., Karahalios, K., & Fu, W.-T. (2018). To Label or Not to Label: The Effect of Stance and Credibility Labels on Readers' Selection and Perception of News Articles. Proc. ACM Hum.-Comput. Interact., 2(CSCW), Article 55. https://doi.org/10.1145/3274324
Garrett, R. K. (2009). Politically Motivated Reinforcement Seeking: Reframing the Selective Exposure Debate. Journal of Communication, 59(4), 676–699. https://doi.org/https://doi.org/10.1111/j.1460-2466.2009.01452.x
Germano, F., Gómez, V., & Sobbrio, F. (2026). Ranking for engagement: How social media algorithms fuel misinformation and polarization. Journal of Public Economics, 255, 105589.
Gillissen, M., Rooduijn, M., & Schumacher, G. (2025). Empathic Concern and Perspective-Taking Have Opposite Effects on Affective Polarization. Journal of Experimental Political Science, 12(2), 218–236. https://doi.org/10.1017/XPS.2024.17
Graf, B., & Antoni, C. H. (2021). The relationship between information characteristics and information overload at the workplace-a meta-analysis. European Journal of Work and Organizational Psychology, 30(1), 143–158.
Guess, A., & Coppock, A. (2020). Does counter-attitudinal information cause backlash? Results from three large survey experiments. British Journal of Political Science, 50(4), 1497–1515.
Guess, A. M., Malhotra, N., Pan, J., Barberá, P., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., Freelon, D., Gentzkow, M., González-Bailón, S., Kennedy, E., Kim, Y. M., Lazer, D., Moehler, D., Nyhan, B., Rivera, C. V., Settle, J., Thomas, D. R.,…Tucker, J. A. (2023). How do social media feed algorithms affect attitudes and behavior in an election campaign? Science, 381(6656), 398–404. https://doi.org/doi:10.1126/science.abp9364
Guo, Y., Lu, Z., Kuang, H., & Wang, C. (2020). Information avoidance behavior on social network sites: Information irrelevance, overload, and the moderating role of time pressure. International Journal of Information Management, 52, 102067.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2–24.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science, 40(3), 414–433.
Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524. https://doi.org/10.1037/0003-066X.44.3.513
Hocevar, K. P., Flanagin, A. J., & Metzger, M. J. (2014). Social media self-efficacy and information evaluation online. Computers in Human Behavior, 39, 254–262. https://doi.org/https://doi.org/10.1016/j.chb.2014.07.020
Holmberg, N., Sandberg, H., & Holmqvist, K. (2014). Advert saliency distracts children's visual attention during task-oriented internet use [Original Research]. Frontiers in Psychology, Volume 5 - 2014. https://doi.org/10.3389/fpsyg.2014.00051
Hong, X., Pan, L., Xu, M., & Chen, Q. (2025). Escaping from the echo chamber: understanding user behavior from the perspective of psychological reactance theory. Information Technology & People, 39(3), 1447–1473. https://doi.org/10.1108/itp-08-2024-0984
Howe, L. C., & Krosnick, J. A. (2017). Attitude strength. Annual Review of Psychology, 68, 327–351.
Howell, D. C. (1992). Statistical methods for psychology. PWS-Kent Publishing Co.
Hsiao, Y.-c., & Yu, E. C.-h. (2020). Polarization Perception and Support for Democracy: The Case of Taiwan. Journal of Asian and African Studies, 55(8), 1143–1162. https://doi.org/10.1177/0021909620911150
Huckfeldt, R., Johnson, P. E., & Sprague, J. (2004). Political disagreement: The survival of diverse opinions within communication networks. Cambridge University Press.
Huckfeldt, R., Mendez, J. M., & Osborn, T. (2004). Disagreement, ambivalence, and engagement: The political consequences of heterogeneous networks. Political Psychology, 25(1), 65–95.
Hwang, E. H., & Lee, S. (2025). A Nudge to Credible Information as a Countermeasure to Misinformation: Evidence from Twitter. INFORMATION SYSTEMS RESEARCH, 36(1), 621–636. https://doi.org/https://doi.org/10.1287/isre.2021.0491
Hyun, K. D., Jung, N., & Seo, M. (2024). Perceptions of opinion climate in online comments and among the general public: Examining the roles of personal opinion, political knowledge, and comment reading. International Journal of Communication, 18, 19–19.
Ilves, M., Syrjämäki, A. H., Olsson, T., Kiskola, J., Isokoski, P., Rantasila, A., Bente, G., & Surakka, V. (2025). Emotion labelling strengthens readers’ emotional responses to online messages. Behaviour & Information Technology, 44(9), 1860–1871. https://doi.org/10.1080/0144929X.2024.2379552
Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S. J. (2019). The Origins and Consequences of Affective Polarization in the United States. Annual Review of Political Science, 22(Volume 22, 2019), 129–146. https://doi.org/https://doi.org/10.1146/annurev-polisci-051117-073034
Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, Not Ideology: A Social Identity Perspective on Polarization. Public Opinion Quarterly, 76(3), 405–431. https://doi.org/10.1093/poq/nfs038
Jiang, T., Sun, Z., & Fu, S. (2025). Restraining the formation of filter bubbles with algorithmic affordances: Toward more balanced information consumption and decreased attitude extremity. Journal of the Association for Information Science and Technology, 76(7), 989–1005. https://doi.org/https://doi.org/10.1002/asi.24988
Johnson, B. K., Neo, R. L., Heijnen, M. E. M., Smits, L., & van Veen, C. (2020). Issues, involvement, and influence: Effects of selective exposure and sharing on polarization and participation. Computers in Human Behavior, 104, 106155. https://doi.org/https://doi.org/10.1016/j.chb.2019.09.031
Kalogeropoulos, A., Negredo, S., Picone, I., & Nielsen, R. K. (2017). Who Shares and Comments on News?: A Cross-National Comparative Analysis of Online and Social Media Participation. Social Media + Society, 3(4), 2056305117735754. https://doi.org/10.1177/2056305117735754
Karnowski, V., Kümpel, A. S., Leonhard, L., & Leiner, D. J. (2017). From incidental news exposure to news engagement. How perceptions of the news post and news usage patterns influence engagement with news articles encountered on Facebook. Computers in Human Behavior, 76, 42–50. https://doi.org/https://doi.org/10.1016/j.chb.2017.06.041
Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061–1072. https://doi.org/https://doi.org/10.1016/j.chb.2010.03.008
Kim, J. W. (2018). They liked and shared: Effects of social media virality metrics on perceptions of message influence and behavioral intentions. Computers in Human Behavior, 84, 153–161.
Kim, Y. (2015). Does disagreement mitigate polarization? How selective exposure and disagreement affect political polarization. Journalism & Mass Communication Quarterly, 92(4), 915–937.
Kim, Y., & Kim, Y. (2019). Incivility on Facebook and political polarization: The mediating role of seeking further comments and negative emotion. Computers in Human Behavior, 99, 219–227. https://doi.org/https://doi.org/10.1016/j.chb.2019.05.022
Kiskola, J., Olsson, T., Syrjämäki, A. H., Rantasila, A., Ilves, M., Isokoski, P., & Surakka, V. (2022). Online Survey on Novel Designs for Supporting Self-Reflection and Emotion Regulation in Online News Commenting. Proceedings of the 25th International Academic Mindtrek Conference, Tampere, Finland. https://doi.org/10.1145/3569219.3569411
Klapp, O. E. (1986). Overload and boredom: Essays on the quality of life in the information society. Greenwood Publishing Group Inc.
Klausegger, C., Sinkovics, R. R., & Zou, H. (2007). Information overload: a cross‐national investigation of influence factors and effects. Marketing Intelligence & Planning, 25(7), 691–718.
Koroleva, K., Krasnova, H., & Günther, O. (2010). 'STOP SPAMMING ME!' - Exploring Information Overload on Facebook. Americas Conference on Information Systems,
Krosnick, J. A., & Petty, R. E. (2014). Attitude strength: An overview. Attitude strength, 1–24.
Kubin, E., & von Sikorski, C. (2021). The Role of (Social) Media in Political Polarization: A Systematic Review. Annals of the international communication association, 45(3), 188–206. https://doi.org/10.1080/23808985.2021.1976070
Kubin, E., & von Sikorski, C. (2024). The polarizing content warning: how the media can reduce affective polarization. Human Communication Research, 50(3), 404–418. https://doi.org/10.1093/hcr/hqae006
Kunda, Z. (1990). The case for motivated reasoning. Psychol Bull, 108(3), 480–498. https://doi.org/10.1037/0033-2909.108.3.480
Lee, A. R., Son, S.-M., & Kim, K. K. (2016). Information and communication technology overload and social networking service fatigue: A stress perspective. Computers in Human Behavior, 55, 51–61. https://doi.org/https://doi.org/10.1016/j.chb.2015.08.011
Lee, E.-J., Jang, Y. J., & Chung, M. (2021). When and How User Comments Affect News Readers’ Personal Opinion: Perceived Public Opinion and Perceived News Position as Mediators. Digital Journalism, 9(1), 42–63. https://doi.org/10.1080/21670811.2020.1837638
Lee, H., Kwak, N., & Campbell, S. W. (2015). Hearing the Other Side Revisited: The Joint Workings of Cross-Cutting Discussion and Strong Tie Homogeneity in Facilitating Deliberative and Participatory Democracy. Communication Research, 42(4), 569–596. https://doi.org/10.1177/0093650213483824
Lee, S., & Cho, J. (2023). Hearing and speaking the other side: The roles of expression and opinion climate perception in political polarization. Computers in Human Behavior, 143, 107672. https://doi.org/https://doi.org/10.1016/j.chb.2023.107672
Lee, Y.-J., Hosanagar, K., & Tan, Y. (2015). Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings. Management Science, 61(9), 2241–2258. https://doi.org/10.1287/mnsc.2014.2082
Levy, R. (2021). Social Media, News Consumption, and Polarization: Evidence from a Field Experiment. American economic review, 111(3), 831–870. https://doi.org/10.1257/aer.20191777
Liang, H., & Fu, K.-w. (2017). Information Overload, Similarity, and Redundancy: Unsubscribing Information Sources on Twitter. Journal of computer-mediated communication, 22(1), 1–17. https://doi.org/10.1111/jcc4.12178
Lin, H., Wang, Y., Lee, J., & Kim, Y. (2023). The effects of disagreement and unfriending on political polarization: a moderated-mediation model of cross-cutting discussion on affective polarization via unfriending contingent upon exposure to incivility. Journal of computer-mediated communication, 28(4). https://doi.org/10.1093/jcmc/zmad022
Lu, Y., & Lee, J. K. (2019). Stumbling upon the other side: Incidental learning of counter-attitudinal political information on Facebook. New Media & Society, 21(1), 248–265. https://doi.org/10.1177/1461444818793421
Lv, X., Chen, Y., & Guo, W. (2022). RETRACTED: Adolescents’ Algorithmic Resistance to Short Video APP’s Recommendation: The Dual Mediating Role of Resistance Willingness and Resistance Intention. Frontiers in Psychology, 13, 859597.
Mackie, D. M., Silver, L. A., & Smith, E. R. (2004). Intergroup Emotions: Emotion as an Intergroup Phenomenon. In The social life of emotions. (pp. 227–245). Cambridge University Press. https://doi.org/10.1017/CBO9780511819568.013
Manin, B. (1987). On legitimacy and political deliberation. Political theory, 15(3), 338–368.
Marchal, N. (2022). “Be Nice or Leave Me Alone”: An Intergroup Perspective on Affective Polarization in Online Political Discussions. Communication Research, 49(3), 376–398. https://doi.org/10.1177/00936502211042516
Matthes, J., Nguyen, G., Koban, K., Meerson, R., Khaleghipour, M., Garner, D. S., Bührer, S., & Kirchmair, T. (2025). Discursive consequences of social media hostility: Chilling effects, avoidance, and intervention behaviors of emerging adults in the U.S. and Indonesia. Computers in Human Behavior, 171, 108704. https://doi.org/https://doi.org/10.1016/j.chb.2025.108704
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective. Routledge.
McHugh, M. L. (2013). The chi-square test of independence. Biochemia medica, 23(2), 143–149.
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
Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210–220. https://doi.org/https://doi.org/10.1016/j.pragma.2013.07.012
Modgil, S., Singh, R. K., Gupta, S., & Dennehy, D. (2024). A confirmation bias view on social media induced polarisation during Covid-19. Information Systems Frontiers, 26(2), 417–441.
Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.
Moravec, P. L., Collis, A., & Wolczynski, N. (2023). Countering State-Controlled Media Propaganda Through Labeling: Evidence from Facebook. INFORMATION SYSTEMS RESEARCH, 35(3), 1435–1447. https://doi.org/10.1287/isre.2022.0305
Munson, S. A., & Resnick, P. (2010). Presenting diverse political opinions: how and how much. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, Georgia, USA. https://doi.org/10.1145/1753326.1753543
Mutz, D. C. (1998). Impersonal influence: How perceptions of mass collectives affect political attitudes. Cambridge University Press.
Mutz, D. C. (2002). Cross-cutting social networks: Testing democratic theory in practice. American Political Science Review, 96(1), 111–126.
Mutz, D. C., & Reeves, B. (2005). The new videomalaise: Effects of televised incivility on political trust. American Political Science Review, 99(1), 1–15.
Nemeth, C., & Rogers, J. (1996). Dissent and the search for information. British Journal of Social Psychology, 35(1), 67–76. https://doi.org/https://doi.org/10.1111/j.2044-8309.1996.tb01083.x
Noelle-Neumann, E. (1974). The spiral of silence: A theory of public opinion. Journal of Communication, 24(2), 43–51.
Nyhan, B., Settle, J., Thorson, E., Wojcieszak, M., Barberá, P., Chen, A. Y., Allcott, H., Brown, T., Crespo-Tenorio, A., & Dimmery, D. (2023). Like-minded sources on Facebook are prevalent but not polarizing. Nature, 620(7972), 137–144.
Overgaard, C. S. B. (2024). Perceiving Affective Polarization in the United States: How Social Media Shape Meta-Perceptions and Affective Polarization. Social Media + Society, 10(1), 20563051241232662. https://doi.org/10.1177/20563051241232662
Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. penguin UK.
Parsons, B. M. (2010). Social networks and the affective impact of political disagreement. Political Behavior, 32(2), 181–204.
Pennington, N., & Palagi, J. S. (2023). Examining How Social and Emotional Factors Inform Response to Cross-Cutting Political Views on Social Media. Social Media + Society, 9(4), 20563051231207851. https://doi.org/10.1177/20563051231207851
Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of personality and social psychology, 90(5), 751.
Pettigrew, T. F., & Tropp, L. R. (2008). How does intergroup contact reduce prejudice? Meta-analytic tests of three mediators. European Journal of Social Psychology, 38(6), 922–934. https://doi.org/https://doi.org/10.1002/ejsp.504
Pew Research Center. (2014). Political polarization in the American public. https://www.pewresearch.org/politics/2014/06/12/political-polarization-in-the-american-public/
Rathje, S., Van Bavel, J. J., & Van Der Linden, S. (2021). Out-group animosity drives engagement on social media. Proceedings of the national academy of sciences, 118(26), e2024292118.
Saveski, M., Gillani, N., Yuan, A., Vijayaraghavan, P., & Roy, D. (2022). Perspective-taking to reduce affective polarization on social media. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 885–895.
Savolainen, R. (2007). Filtering and withdrawing: strategies for coping with information overload in everyday contexts. Journal of information Science, 33(5), 611–621.
Schoenmueller, V., Netzer, O., & Stahl, F. (2020). The polarity of online reviews: Prevalence, drivers and implications. Journal of Marketing Research, 57(5), 853–877.
Scorza, J. A. (1998). Uncivil friendship: Emersonian lessons for democratic disagreement. Annual meetings of the American Political Science Association,, Boston, MA.
Sheng, N., Yang, C., Han, L., & Jou, M. (2023). Too much overload and concerns: Antecedents of social media fatigue and the mediating role of emotional exhaustion. Computers in Human Behavior, 139, 107500. https://doi.org/https://doi.org/10.1016/j.chb.2022.107500
Shim, Y., & Jhaver, S. (2025). The Pin of Shame: Examining Content Creators' Adoption of Pinning Inappropriate Comments as a Moderation Strategy. arXiv preprint arXiv:2505.14844.
Smith, E. R. (1993). Chapter 13 - Social Identity and Social Emotions: Toward New Conceptualizations of Prejudice. In D. M. Mackie & D. L. Hamilton (Eds.), Affect, Cognition and Stereotyping (pp. 297–315). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-08-088579-7.50017-X
Sohn, D., & Choi, S. (2019). Social embeddedness of persuasion: effects of cognitive social structures on information credibility assessment and sharing in social media. International Journal of Advertising, 38(6), 824–844. https://doi.org/10.1080/02650487.2018.1536507
Spitz, A., Abu-Akel, A., & West, R. (2021). Interventions for softening can lead to hardening of opinions: evidence from a randomized controlled trial. Proceedings of the Web Conference 2021,
Steindl, C., Jonas, E., Sittenthaler, S., Traut-Mattausch, E., & Greenberg, J. (2015). Understanding Psychological Reactance: New Developments and Findings. Z Psychol, 223(4), 205–214. https://doi.org/10.1027/2151-2604/a000222
Stroud, N. J. (2010). Polarization and Partisan Selective Exposure. Journal of Communication, 60(3), 556–576. https://doi.org/https://doi.org/10.1111/j.1460-2466.2010.01497.x
Stroud, N. J., Feldman, L., Wojcieszak, M., & Bimber, B. (2019). The Consequences of Forced Versus Selected Political Media Exposure. Human Communication Research, 45(1), 27–51. https://doi.org/10.1093/hcr/hqy012
Sude, D., & Knobloch-Westerwick, S. (2022). Selective exposure and attention to attitude-consistent and attitude-discrepant information: Reviewing the evidence. In Knowledge resistance in high-choice information environments (pp. 88–105).
Sude, D. J., Knobloch-Westerwick, S., Robinson, M. J., & Westerwick, A. (2019). “Pick and choose” opinion climate: How browsing of political messages shapes public opinion perceptions and attitudes. Communication Monographs, 86(4), 457–478.
Sundar, S. S., Oeldorf-Hirsch, A., & Xu, Q. (2008). The bandwagon effect of collaborative filtering technology. CHI '08 Extended Abstracts on Human Factors in Computing Systems, Florence, Italy. https://doi.org/10.1145/1358628.1358873
Sunstein, C. R. (2002). The law of group polarization. Journal of political philosophy, 10(2), 175–195. https://doi.org/Doi 10.1111/1467-9760.00148
Sunstein, C. R. (2006). Infotopia How Many Minds Produce Knowledge. Oxford University Press. https://doi.org/10.1093/oso/9780195189285.001.0001
Sunstein, C. R. (2009). Going to Extremes: How Like Minds Unite and Divide. Oxford University Press. https://doi.org/10.1093/oso/9780195378016.001.0001
Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257–285. https://doi.org/https://doi.org/10.1207/s15516709cog1202_4
Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs. American journal of political science, 50(3), 755–769.
Tajfel, H., & Turner, J. C. (1979). An Integrative Theory of Intergroup Conflict. In W. G. A. S. Worchel (Ed.), The Social Psychology of Intergroup Relations (pp. 33–37). Brooks/Cole. https://alnap.org/help-library/resources/an-integrative-theory-of-intergroup-conflict/
Törnberg, P., Valeeva, D., Uitermark, J., & Bail, C. (2023). Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms. arXiv [Preprint]. https://doi.org/https://doi.org/10.48550/arXiv.2310.05984
Tsang, S. J. (2019). Cognitive discrepancy, dissonance, and selective exposure. Media Psychology, 22(3), 394–417.
Tsfati, Y., Stroud, N. J., & Chotiner, A. (2014). Exposure to Ideological News and Perceived Opinion Climate: Testing the Media Effects Component of Spiral-of-Silence in a Fragmented Media Landscape. The International Journal of Press/Politics, 19(1), 3–23. https://doi.org/10.1177/1940161213508206
Turner, J. C., & Reynolds, K. J. (2012). Self-categorization theory. Handbook of theories of social psychology, 2, 399–417.
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 2.
Van der Meer, T. G. L. A., Hameleers, M., & Kroon, A. C. (2020). Crafting Our Own Biased Media Diets: The Effects of Confirmation, Source, and Negativity Bias on Selective Attendance to Online News. Mass Communication and Society, 23(6), 937–967. https://doi.org/10.1080/15205436.2020.1782432
Wang, C., Zhang, X., & Hann, I.-H. (2018). Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings. INFORMATION SYSTEMS RESEARCH, 29(3), 641–655. https://doi.org/10.1287/isre.2017.0741
Wang, J., Zheng, B., Liu, H., & Yu, L. (2021). A two-factor theoretical model of social media discontinuance: role of regret, inertia, and their antecedents. Information Technology & People, 34(1), 1–24. https://doi.org/10.1108/itp-10-2018-0483
Wang, L., Wang, C., & Yao, X. (2024). Befriended to polarise? The impact of friend identity on review polarisation—A quasi-experiment. Information Systems Journal, 34(3), 680–710. https://doi.org/https://doi.org/10.1111/isj.12425
Wang, Z., Li, H., Ye, Q., & Law, R. (2016). Saliency effects of online reviews embedded in the description on sales: Moderating role of reputation. Decision Support Systems, 87, 50–58.
Webster, S. W., & Abramowitz, A. I. (2017). The Ideological Foundations of Affective Polarization in the U.S. Electorate. American Politics Research, 45(4), 621–647. https://doi.org/10.1177/1532673x17703132
Winter, K., Zapf, B., Hütter, M., Tichy, N., & Sassenberg, K. (2021). Selective exposure in action: Do visitors of product evaluation portals select reviews in a biased manner? Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 15(1).
Wojcieszak, M., & Warner, B. R. (2020). Can Interparty Contact Reduce Affective Polarization? A Systematic Test of Different Forms of Intergroup Contact. Political Communication, 37(6), 789–811. https://doi.org/10.1080/10584609.2020.1760406
Yi, C., Jiang, Z., Li, X., & Lu, X. (2019). Leveraging user-generated content for product promotion: The effects of firm-highlighted reviews. INFORMATION SYSTEMS RESEARCH, 30(3), 711–725.
Zerback, T., & Kobilke, L. (2024). The role of affective and cognitive attitude extremity in perceived viewpoint diversity exposure. New Media & Society, 26(7), 4183–4200. https://doi.org/10.1177/14614448221117484
Zhao, L., & Lu, Y. (2012). Enhancing perceived interactivity through network externalities: An empirical study on micro-blogging service satisfaction and continuance intention. Decision Support Systems, 53(4), 825–834. https://doi.org/https://doi.org/10.1016/j.dss.2012.05.019