A rainbow Hurricane?: Exploring student evaluations of ambiguously credible tweeted information within crisis contexts

References

• Austin, L., Kim, S., & Saffer, A. J. (2023). Emotion as a predictor of crisis communicative behaviors: Examining information seeking and sharing during Hurricane Florence. Journal of Applied Communication Research, 51(5), 559–578. https://doi.org/10.1080/00909882.2023.2177121
   Web of Science ®Google Scholar
• Avery, E. J., & Park, S. (2021). Perceived knowledge as [protective] power: Parents’ protective efficacy, information-seeking, and scrutiny during COVID-19. Health Communication, 36(1), 81–88. https://doi.org/10.1080/10410236.2020.1847438
   PubMed Web of Science ®Google Scholar
• boyd, d. (2014). It’s complicated: The social lives of networked teens. Yale University Press.
   Google Scholar
• Brand-Gruwel, S., Kammerer, Y., van Meeuwen, L., & van Gog, T. (2017). Source evaluation of domain experts and novices during Web search. Journal of Computer Assisted Learning, 33(3), 234–251. https://doi.org/10.1111/jcal.12162
   Web of Science ®Google Scholar
• Bråten, I., McCrudden, M. T., Stang Lund, E., Brante, E. W., & Strømsø, H. I. (2018). Task-oriented learning with multiple documents: effects of topic familiarity, author expertise, and content relevance on document selection, processing, and use. Reading Research Quarterly, 53(3), 345–365. https://doi.org/10.1002/rrq.197
   Web of Science ®Google Scholar
• Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766. https://doi.org/10.1037/0022-3514.39.5.752
   Web of Science ®Google Scholar
• Cho, B.-Y. (2014). Competent adolescent readers’ use of internet reading strategies: A think-aloud study. Cognition and Instruction, 32(3), 253–289. https://doi.org/10.1080/07370008.2014.918133
   Web of Science ®Google Scholar
• Coiro, J. (2021). Toward a multifaceted heuristic of digital reading to inform assessment, research, practice, and policy. Reading Research Quarterly, 56(1), 9–31. https://doi.org/10.1002/rrq.302
   Web of Science ®Google Scholar
• Coiro, J., Coscarelli, C., Maykel, C., & Forzani, E. (2015). Investigating criteria that seventh graders use to evaluate the quality of online information. Journal of Adolescent & Adult Literacy, 59(3), 287–297. https://doi.org/10.1002/jaal.448
   Web of Science ®Google Scholar
• Cooks, E. J., Vilaro, M. J., Dyal, B. W., Wang, S., Mertens, G., Raisa, A., Kim, B., Campbell-Salome, G., Wilkie, D. J., Odedina, F., Johnson-Mallard, V., Yao, Y., & Krieger, J. L. (2022). What did the pandemic teach us about effective health communication? Unpacking the COVID-19 infodemic. BMC Public Health, 22(1), 2339. https://doi.org/10.1186/s12889-022-14707-3
   PubMed Web of Science ®Google Scholar
• Drouin, M., McDaniel, B. T., Pater, J., & Toscos, T. (2020). How parents and their children used social media and technology at the beginning of the COVID-19 pandemic and associations with anxiety. Cyberpsychology, Behavior and Social Networking, 23(11), 727–736. https://doi.org/10.1089/cyber.2020.0284
   PubMed Web of Science ®Google Scholar
• Edgerly, S., & Vraga, E. K. (2019). The blue check of credibility: Does account verification matter when evaluating news on Twitter? Cyberpsychology, Behavior and Social Networking, 22(4), 283–287. https://doi.org/10.1089/cyber.2018.0475
   PubMed Web of Science ®Google Scholar
• Evon, D. (2019, 4 September). Is this the eye of Hurricane Dorian? Snopes. https://www.snopes.com/fact-check/colorful-eye-hurricane-dorian/
   Google Scholar
• Flanagin, A. J., & Metzger, M. J. (2013). Trusting expert- versus user-generated ratings online: The role of information volume, valence, and consumer characteristics. Computers in Human Behavior, 29(4), 1626–1634. https://doi.org/10.1016/j.chb.2013.02.001
   Web of Science ®Google Scholar
• Flanagin, A. J., Metzger, M. J., Hartsell, E., Markov, A., Medders, R., Pure, R., & Choi, E. (2010). Kids and credibility: An empirical examination of youth, digital media use, and information credibility. MIT Press.
   Google Scholar
• Forzani, E. (2020). A three-tiered framework for proactive critical evaluation during online inquiry. Journal of Adolescent & Adult Literacy, 63(4), 401–414. https://doi.org/10.1002/jaal.1004
   Web of Science ®Google Scholar
• Forzani, E., Corrigan, J., & Kiili, C. (2022). What does more and less effective internet evaluation entail?: Investigating readers’ credibility judgments across content, source, and context. Computers in Human Behavior, 135, 107359. https://doi.org/10.1016/j.chb.2022.107359
   Web of Science ®Google Scholar
• Gillespie, T. (2018). Custodians of the internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press.
   Google Scholar
• Guillory, J. J., & Geraci, L. (2013). Correcting erroneous inferences in memory: The role of source credibility. Journal of Applied Research in Memory and Cognition, 2(4), 201–209. https://doi.org/10.1016/j.jarmac.2013.10.001
   Web of Science ®Google Scholar
• Hagar, C. (2015). Crisis informatics. Encyclopedia of information science and technology (3rd ed., pp. 1350–1358). IGI Global.
   Google Scholar
• Hunt, K., Wang, B., & Zhuang, J. (2020). Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: A case study on shelters and ID checks. Natural Hazards, 103(1), 861–883. https://doi.org/10.1007/s11069-020-04016-6
   Web of Science ®Google Scholar
• Hyman, I. E., & Jalbert, M. C. (2017). Misinformation and worldviews in the post-truth information age: Commentary on Lewandowsky, Ecker, and Cook. Journal of Applied Research in Memory and Cognition, 6(4), 377–381. https://doi.org/10.1016/j.jarmac.2017.09.009
   Web of Science ®Google Scholar
• Kammerer, Y., Gottschling, S., & Bråten, I. (2021). The role of internet-specific justification beliefs in source evaluation and corroboration during web search on an unsettled socio-scientific issue. Journal of Educational Computing Research, 59(2), 342–378. https://doi.org/10.1177/0735633120952731
   Web of Science ®Google Scholar
• Kiili, C., Räikkönen, E., Bråten, I., Strømsø, H. I., & Hagerman, M. S. (2023). Examining the structure of credibility evaluation when sixth graders read online texts. Journal of Computer Assisted Learning, 39(3), 954–969. https://doi.org/10.1111/jcal.12779
   Web of Science ®Google Scholar
• Lachlan, K. A., Hutter, E., Gilbert, C., & Spence, P. R. (2021). From what I’ve heard, this is bad: An examination of Americans’ source preferences and information seeking during the COVID-19 pandemic. Progress in Disaster Science, 9, 100145. https://doi.org/10.1016/j.pdisas.2021.100145
   Google Scholar
• Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017). Beyond misinformation: Understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition, 6(4), 353–369. https://doi.org/10.1016/j.jarmac.2017.07.008
   Web of Science ®Google Scholar
• Lin, X., Spence, P. R., & Lachlan, K. A. (2016). Social media and credibility indicators: The effect of influence cues. Computers in Human Behavior, 63, 264–271. https://doi.org/10.1016/j.chb.2016.05.002
   Web of Science ®Google Scholar
• Liu, B. F.,Bartz, L., &Duke, N. (2016). Communicating crisis uncertainty: A review of the knowledge gaps. Public Relations Review, 42(3), 479–487. https://doi.org/10.1016/j.pubrev.2016.03.003
   Web of Science ®Google Scholar
• Lucassen, T., & Schraagen, J. M. (2013). The influence of source cues and topic familiarity on credibility evaluation. Computers in Human Behavior, 29(4), 1387–1392. https://doi.org/10.1016/j.chb.2013.01.036
   Web of Science ®Google Scholar
• Madden, M., Fox, S. (2006, October 5). Riding the waves of “Web 2.0”: More than a buzzword, but still not easily defined. Pew Research Center. http://www.pewInternet.org/pdfs/PIP Web 2.0.pdf
   Google Scholar
• Maheswaran, D., & Chaiken, S. (1991). Promoting systematic processing in low-motivation settings: Effect of incongruent information on processing and judgment. Journal of Personality and Social Psychology, 61(1), 13–25. https://doi.org/10.1037/0022-3514.61.1.13
   PubMed Web of Science ®Google Scholar
• Martens, H., & Hobbs, R. (2015). How media literacy supports civic engagement in a digital age. Atlantic Journal of Communication, 23(2), 120–137. https://doi.org/10.1080/15456870.2014.961636
   Web of Science ®Google Scholar
• McGrew, S. (2020). Learning to evaluate: An intervention in civic online reasoning. Computers & Education, 145, 103711. https://doi.org/10.1016/j.compedu.2019.103711
   Web of Science ®Google Scholar
• McGrew, S., Breakstone, J., Ortega, T., Smith, M., & Wineburg, S. (2018). Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory & Research in Social Education, 46(2), 165–193. https://doi.org/10.1080/00933104.2017.1416320
   Web of Science ®Google Scholar
• Mertens, G. E., Mundorf, J., Bainter, T., Bourn, J., & Kohnen, A. M. (2021). Wisdom begins in wonder: Implementing identity-based information literacy instruction across the content areas. Middle School Journal, 52(2), 38–47. https://doi.org/10.1080/00940771.2020.1868059
   Google Scholar
• Mertens, G. E., & Kohnen, A. (2022). Critically reading internet architecture. English Journal, 111(4), 62–70. https://doi.org/10.58680/ej2022.39614
   Google Scholar
• Metzger, M. J., Flanagin, A. J., Medders, R., Pure, R., Markov, A., & Hartsell, E. (2013). The special case of youth and digital information credibility. In M. Folk & S. Apostel (Eds.), Online credibility and digital ethos: Evaluating computer-mediated communication (pp. 148–168). IGI Global.
   Google Scholar
• Metzger, M. J.,Flanagin, A. J., &Medders, R. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413–439. https://doi.org/10.1111/j.1460-2466.2010.01488.x
   Web of Science ®Google Scholar
• Nahon, K., & Hemsley, J. (2014). Going viral (1st ed.). Wiley.
   Google Scholar
• O’Mary, L. (2023, January 30). Surgeon general says 13-year-olds shouldn’t be on social media. WebMD. https://www.webmd.com/children/news/20230130/surgeon-general-says-13-year-olds-shouldnt-be-on-social-media
   Google Scholar
• O’Keeffe, G. S. (2016). Social media: Challenges and concerns for families. Pediatric Clinics of North America, 63(5), 841–849. https://doi.org/10.1016/j.pcl.2016.06.009
   PubMed Web of Science ®Google Scholar
• Oh, O., Kwon, K., Rao, H. (2010). An exploration of social media in extreme events: Rumor theory and Twitter during the Haiti earthquake 2010. ICIS 2010 Proceedings. https://aisel.aisnet.org/icis2010_submissions/231
   Google Scholar
• Pilgrim, J., Vasinda, S., Bledsoe, C., & Martinez, E. (2019). Critical thinking is critical: Octopuses, online sources, and reliability reasoning. The Reading Teacher, 73(1), 85–93. https://doi.org/10.1002/trtr.1800
   Web of Science ®Google Scholar
• Ritzhaupt, A. D., Kohnen, A. M., Wusylko, C., Wang, X., Dawson, K., & Sommer, M. (2023). The role of skepticism along adolescents’ online information literacy skills. Information and Learning Sciences. Advance online publication. https://doi.org/10.1108/ILS-02-2023-0010
   Web of Science ®Google Scholar
• Rubin, V. L. (2017). Deception detection and rumor debunking for social media. In L. Sloan & A. Quan-Haase (Eds.), The SAGE handbook of social media research methods (pp. 342–364). SAGE.
   Google Scholar
• Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). SAGE Publishing.
   Google Scholar
• Seifert, C. M. (2017). The distributed influence of misinformation. Journal of Applied Research in Memory and Cognition, 6(4), 397–400. https://doi.org/10.1016/j.jarmac.2017.09.003
   Web of Science ®Google Scholar
• Shin, J., Jian, L., Driscoll, K., & Bar, F. (2018). The diffusion of misinformation on social media: Temporal pattern, message, and source. Computers in Human Behavior, 83, 278–287. https://doi.org/10.1016/j.chb.2018.02.008
   Web of Science ®Google Scholar
• Son, J.,Lee, J.,Oh, O.,Lee, H. K., &Woo, J. (2020). Using a Heuristic-Systematic Model to assess the Twitter user profile’s impact on disaster tweet credibility. International Journal of Information Management, 54, Article 102176.https://doi.org/10.1016/j.ijinfomgt.2020.102176
   Web of Science ®Google Scholar
• Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. J. Metzger & A. J. Flanagin (Eds.), Digital media, youth, and credibility (pp. 73–100). MIT Press.
   Google Scholar
• Sundar, S. S., Jia, H., Waddell, T. F., & Huang, Y. (2015). Toward a Theory of Interactive Media Effects (TIME): Four models for explaining how interface features affect user psychology. In S. S. Sundar (Ed.), The handbook of the psychology of communication technology (1st ed., pp. 47–86). Wiley. https://doi.org/10.1002/9781118426456.ch3
   Google Scholar
• Taylor, K.,Zarb, S., &Jeschke, N. (2021). Ambiguity, uncertainty and implementation. International Review of Public Policy, 3(1). https://doi.org/10.4000/irpp.1638
   Google Scholar
• van den Bosch, A., Bogers, T., & de Kunder, M. (2016). Estimating search engine index size variability: A 9-year longitudinal study. Scientometrics, 107(2), 839–856. https://doi.org/10.1007/s11192-016-1863-z
   PubMed Web of Science ®Google Scholar
• Waddell, T. F. (2018). What does the crowd think? How online comments and popularity metrics affect news credibility and issue importance. New Media & Society, 20(8), 3068–3083. https://doi.org/10.1177/1461444817742905
   Web of Science ®Google Scholar
• Wang, B., & Zhuang, J. (2018). Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters. Natural Hazards, 93(3), 1145–1162. https://doi.org/10.1007/s11069-018-3344-6
   Web of Science ®Google Scholar
• Wineburg, S., & McGrew, S. (2019). Lateral reading and the nature of expertise: Reading less and learning more when evaluating digital information. Teachers College Record: The Voice of Scholarship in Education, 121(11), 1–40. https://doi.org/10.1177/016146811912101102
   PubMed Web of Science ®Google Scholar
• Yaffe-Bellany, D. (2022, November 5). Twitter begins offering $7.99-a-month verification subscriptions. The New York Times. https://www.nytimes.com/2022/11/05/business/twitter-verification-check-marks.html
   Google Scholar
• Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.
   Google Scholar