Examining the effectiveness of social media warning labels

The role of worldview inconsistency and reactance

Authors

  • Bingbing Zhang University of Iowa

Keywords:

warning label, misinformation, worldview inconsistency, psychological reactance, perceived credibility, intention to share

Abstract

Social media platforms frequently employ warning labels to identify potentially misleading information, yet research has yielded inconsistent findings regarding the efficacy of these labels. This study aimed to assess the effectiveness of two prominent types of Twitter (which is rebranded as X) warning labels in the specific context of identifying misinformation related to immigration. The results revealed that the warning labels had no direct impact on diminishing perceived credibility and the intention to share the misinformation post. However, the findings did highlight a moderating effect of worldview inconsistency. The research findings provide valuable insights for effective designs for corrective messaging on social media platforms, emphasizing the role of worldview inconsistency and reactance on processing various warning label types in discrediting misinformation posts.

Author Biography

Bingbing Zhang, University of Iowa

Bingbing Zhang (PhD, Pennsylvania State University) currently is a assistant professor in the School of Journalism and Mass Communication at the University of Iowa. Her research interests focus on media effects regarding how media messages impact individuals’ beliefs, attitudes, and behaviors. Using the quantitative method, she looks at how media messages addressing political, health, and science issues impact people’s processing of information, and attitudinal and behavioral changes.

References

Allcott, H., Gentzkow, M., & Yu, C. (2019). Trends in the diffusion of misinformation on social media. Research & Politics, 6(2), 1-8. https://doi.org/10.1177/2053168019848554

Amazeen, M. A., Thorson, E., Muddiman, A., & Graves, L. (2018). Correcting political and consumer misperceptions: The effectiveness and effects of rating scale versus contextual correction formats. Journalism & Mass Communication Quarterly, 95(1), 28-48. https://doi.org/10.1177/1077699016678186

Amazeen, M. A. (2020). Journalistic interventions: The structural factors affecting the global emergence of fact-checking. Journalism, 21(1), 95-111. https://doi.org/10.1177/1464884917730217

Berinsky, A. J. (2015). Rumors and health care reform: Experiments in political misinformation. British Journal of Political Science, 47(2), 241-262. https://doi.org/10.1017/s0007123415000186

Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Science vs conspiracy: Collective narratives in the age of misinformation. PloS one, 10(2), e0118093. https://doi.org/10.1371/journal.pone.0118093

Bode, L., & Vraga, E. K. (2015). In related news, that was wrong: The correction of misinformation through related stories functionality in social media. Journal of Communication, 65(4), 619-638. https://doi.org/10.1111/jcom.12166

Brehm, J. W. (1966). A theory of psychological reactance. New York, NY: Academic Press.

Brehm, S. S., & Brehm, J. W. (1981). Psychological reactance: A theory of freedom and control. San Diego, CA: Academic Press.

Clayton, K., Blair, S., Busam, J. A., Forstner, S., Glance, J., Green, G., ... & Nyhan, B. (2020). Real solutions for fake news? Measuring the effectiveness of general warnings and fact-check tags in reducing belief in false stories on social media. Political Behavior, 42(4), 1073-1095. https://doi.org/10.1007/s11109-019-09533-0

Fransen, M. L., Smit, E. G., & Verlegh, P. W. (2015). Strategies and motives for resistance to persuasion: an integrative framework. Frontiers in Psychology, 6, 1201. https://doi.org/10.3389/fpsyg.2015.01201

Freeze, M., Baumgartner, M., Bruno, P., Gunderson, J. R., Olin, J., Ross, M. Q., & Szafran, J. (2020). Fake claims of fake news: political misinformation, warnings, and the tainted truth Effect. Political Behavior, 43, 1-33. https://doi.org/10.1007/s11109-020-09597-3

Garrett, R. K., & Poulsen, S. (2019). Flagging facebook falsehoods: Self-identified humor warnings outperform fact checker and peer warnings. Journal of Computer-Mediated Communication, 24(5), 240-258. https://doi.org/10.1093/jcmc/zmz012

Hameleers, M., & van der Meer, T. G. (2020). Misinformation and polarization in a high-choice media environment: How effective are political fact-checkers?. Communication Research, 47(2), 227-250. https://doi.org/10.1177/0093650218819671

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, London: Guilford Press.

Jackson, S., O'Keefe, D. J., & Brashers, D. E. (1994). The messages replication factor: Methods tailored to messages as objects of study. Journalism Quarterly, 71(4), 984-996. https://doi.org/10.1177/107769909407100421

Kahan, D. (2010). Fixing the communications failure. Nature, 463, 296-297.

Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106-131. https://doi.org/10.1177/1529100612451018

Maertens, R., Roozenbeek, J., Basol, M., & van der Linden, S. (2021). Long-term effectiveness of inoculation against misinformation: Three longitudinal experiments. Journal of Experimental Psychology: Applied, 27(1), 1-16. https://doi.org/10.1037/xap0000315

Mena, P. (2020). Cleaning up social media: The effect of warning labels on likelihood of sharing false news on Facebook. Policy & internet, 12(2), 165-183. https://doi.org/10.1002/poi3.214

Moravec, P., Minas, R., & Dennis, A. R. (2018). Fake news on social media: People believe what they want to believe when it makes no sense at all. Kelley School of Business Research Paper, (18-87). http://dx.doi.org/10.2139/ssrn.3269541

Nassetta, J., & Gross, K. (2020). State media warning labels can counteract the effects of foreign misinformation. Harvard Kennedy School Misinformation Review. Accessed at https://misinforeview.hks.harvard.edu/article/state-media-warning-labels-can-counteract-the-effects-of-foreign-misinformation/

Novak, V. (2021). Does Immigration Cost Jobs?. Retrieved from https://www.factcheck.org/2010/05/does-immigration-cost-jobs/

Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303-330. https://www.jstor.org/stable/40587320

Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50(3), 755–769. https://doi.org/10.1111/j.1540-5907.2006.00214.x

Vraga, E. K., Kim, S. C., Cook, J., & Bode, L. (2020). Testing the Effectiveness of Correction Placement and Type on Instagram. The International Journal of Press/Politics, 25(4), 632-652. https://doi.org/10.1177/1940161220919082

Walter, N., Cohen, J., Holbert, R. L., & Morag, Y. (2020). Fact-checking: A meta-analysis of what works and for whom. Political Communication, 37(3), 350-375. https://doi.org/10.1080/10584609.2019.1668894

Oeldorf-Hirsch, A., Schmierbach, M., Appelman, A., & Boyle, M. P. (2020). The ineffectiveness of fact-checking labels on news memes and articles. Mass Communication & Society, 23(5), 682-704. https://doi.org/10.1080/15205436.2020.1733613

Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39-50. https://doi.org/10.1016/j.cognition.2018.06.011

Quick, B. L., Shen, L., & Dillard, J. P. (2013). Reactance theory and persuasion. In The SAGE handbook of persuasion: Developments in theory and practice (pp. 167-183).

Smith, J. (2017, December 20th). Designing Against Misinformation. Retrieved from https://medium.com/facebook-design/designing-against-misinformation-e5846b3aa1e2

Sheehan, K. B. (2018). Crowdsourcing research: data collection with Amazon’s Mechanical Turk. Communication Monographs, 85(1), 140-156. https://doi.org/10.1080/03637751.2017.1342043

Stroud, N. J. (2017). Selective exposure theories. In The Oxford handbook of political communication.

Sundar, S. S., Knobloch-Westerwick, S., & Hastall, M. R. (2007). News cues: Information scent and cognitive heuristics. Journal of the American Society for Information Science and Technology, 58(3), 366–378. http://dx.doi.org/10.1002/asi.20511.

Sears, D. O., & Freedman, J. L. (1967). Selective exposure to information: A critical review. Public Opinion Quarterly, 31(2), 194–213. https://doi.org/10.1086/267513

Sharevski, F., Alsaadi, R., Jachim, P., & Pieroni, E. (2021). Misinformation warning labels: Twitter’s soft moderation effects on COVID-19 vaccine belief echoes. arXiv preprint arXiv:2104.00779.

Roth, Y. & Pickles (2020, May 11th). Updating our approach to misleading information. https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information.html

Taylor, S., & Asmundson, G. J. G. (2021). Negative attitudes about facemasks during the COVID-19 pandemic: The dual importance of perceived ineffectiveness and psychological reactance. PLoS One, 16(2), e0246317. https://doi.org/10.1371/journal.pone.0246317

Thorson, E. (2016). Belief echoes: The persistent effects of corrected misinformation. Political Communication, 33(3), 460-480. https://doi.org/10.1080/10584609.2015.1102187

Walter, N., & Murphy, S. T. (2018). How to unring the bell: A meta-analytic approach to correction of misinformation. Communication Monographs, 85(3), 423-441. https://doi.org/10.1080/03637751.2018.1467564

Wood, T., & Porter, E. (2019). The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Political Behavior, 41(1), 135-163. https://doi.org/10.1007/s11109-018-9443-y

Downloads

Published

2024-12-31

Issue

Vol. 13 No. 2 (2024): Journal of Social Media in Society

Section

Articles

License

Authors who publish with this journal agree to the following terms:
  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).