The public’s appropriation of multimodal discourses of fake news on social media

References

• Alfano, M., Carter, J. A., & Cheong, M. (2018). Technological seduction and self-radicalization. Journal of the American Philosophical Association, 4(3), 298–322. doi:10.1017/apa.2018.27
   Web of Science ®Google Scholar
• Al-Rawi, A. (2021). How did Russian and Iranian trolls’ disinformation toward Canadian issues diverge and converge? Digital War, 2(1–3), 21–34. doi:10.1057/s42984-020-00029-4
   Google Scholar
• Al-Rawi, A. (2022). Hashtagged trolling and emojified hate against muslims on social media. Religions, 13(6), 521. doi:10.3390/rel13060521
   Web of Science ®Google Scholar
• Alrumaih, A., Al-Sabbagh, A., Alsabah, R., Kharrufa, H., & Baldwin, J. (2020). Sentiment analysis of comments in social media. International Journal of Electrical & Computer Engineering, 10(6), 2088–8708. doi:10.11591/ijece.v10i6.pp5917-5922
   Google Scholar
• Amarasingam, A. (Ed.). (2014). The Stewart/Colbert effect: Essays on the real impacts of fake news. Jefferson, North Carolina: McFarland.
   Google Scholar
• Au, C. H., Ho, K. K., & Chiu, D. K. (2021). The role of online misinformation and fake news in ideological polarization: Barriers, catalysts, and implications. Information Systems Frontiers, 24(4), 1–24. doi:10.1007/s10796-021-10133-9
   Web of Science ®Google Scholar
• Azzimonti, M., & Fernandes, M. (2018). Social media networks, fake news, and polarization (No. w24462). National Bureau of Economic Research. https://www.nber.org/papers/w24462
   Google Scholar
• Badawy, A., Ferrara, E., & Lerman, K. (2018). Analyzing the digital traces of political manipulation: The 2016 Russian interference Twitter campaign. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 258–265.
   Google Scholar
• Bail,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. doi:10.1073/pnas.1804840115
   PubMed Web of Science ®Google Scholar
• Baym, G. (2005). The Daily Show: Discursive integration and the reinvention of political journalism. Political Communication, 22(3), 259–276. doi:10.1080/10584600591006492
   Web of Science ®Google Scholar
• Benkler, Y., Faris, R., Roberts, H., & Zuckerman, E. (2017). Study: Breitbart-led right-wing media ecosystem altered broader media agenda. Columbia Journalism Review. Retrieved from https://www.cjr.org/analysis/breitbart-media-trump-harvard-study.php
   Google Scholar
• Berger, A. (2004). Media analysis techniques. California: SAGE.
   Google Scholar
• Bossetta, M. (2018). The digital architectures of social media: Comparing political campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 US election. Journalism & Mass Communication Quarterly, 95(2), 471–496. doi:10.1177/1077699018763307
   Web of Science ®Google Scholar
• Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 1–14. doi:10.1038/s41467-018-07761-2
   PubMed Web of Science ®Google Scholar
• Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313–7318. doi:10.1073/pnas.1618923114
   PubMed Web of Science ®Google Scholar
• Brummette, J., DiStaso, M., Vafeiadis, M., & Messner, M. (2018). Read all about it: The politicization of “Fake News” on Twitter. Journalism & Mass Communication Quarterly, 95(2), 497–517. doi:10.1177/1077699018769906
   Web of Science ®Google Scholar
• Castillo, C., Mendoza, M., & Poblete, B. (2011, March). Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, Hyderabad, India, (pp. 675–684).
   Google Scholar
• Chalabi, M. (2014). The 100 most-used emojis. FiveThirtyeight. Retrieved from: https://fivethirtyeight.com/features/the-100-most-used-emojis/
   Google Scholar
• Chandler, K. J. (2020). Where we go 1 we go all: A public discourse analysis of QAnon. McNair Scholars Research Journal, 13(1), 4.
   Google Scholar
• Dubé, E., Laberge, C., Guay, M., Bramadat, P., Roy, R., & Bettinger, J. A. (2013). Vaccine hesitancy: An overview. Human Vaccines & Immunotherapeutics, 9(8), 1763–1773. doi:10.4161/hv.24657
   PubMed Web of Science ®Google Scholar
• Egelhofer, J. L., Aaldering, L., Eberl, J. M., Galyga, S., & Lecheler, S. (2020). From novelty to normalization? How journalists use the term “fake news” in their reporting. Journalism Studies, 21(10), 1323–1343. doi:10.1080/1461670X.2020.1745667
   Web of Science ®Google Scholar
• Farhall, K., Carson, A., Wright, S., Gibbons, A., & Lukamto, W. (2019). Political elites’ use of fake news discourse across communications platforms. International Journal of Communication, 13(23), 4353–4375.
   Google Scholar
• Farkas, J., & Schou, J. (2018). Fake news as a floating signifier: Hegemony, antagonism and the politics of falsehood. Javnost - the Public, 25(3), 298–314. doi:10.1080/13183222.2018.1463047
   Web of Science ®Google Scholar
• Franzosi, R. (2017) Content analysis. In R. Wodak & B. Forchtner, (Eds.) The Routledge Handbook of Language and Politics (pp. 153–168). London: Routledge. 10.4324/9781315183718-13
   Google Scholar
• Freelon, D. (2010). ReCal: Intercoder reliability calculation as a web service. International Journal of Internet Science, 5(1), 20–33.
   Google Scholar
• Gray, J., Jones, J. P., & Thompson, E. (Eds.). (2009). Satire TV: Politics and comedy in the post-network era. New York: NYU Press.
   Google Scholar
• Hirst, M. (2017). Towards a political economy of fake news. The Political Economy of Communication, 5(2), 82–94.
   Google Scholar
• Hodson, J., & Petersen, B. (2019). Diversity in Canadian election-related Twitter discourses: Influential voices and the media logic of #elxn42 and #cdnpoli hashtags. Journal of Information Technology & Politics, 16(3), 307–323. doi:10.1080/19331681.2019.1646181
   Web of Science ®Google Scholar
• Huey, L. (2015). This is not your mother’s terrorism: Social media, online radicalization and the practice of political jamming. Journal of Terrorism Research, 6(2). doi:10.15664/jtr.1159
   Google Scholar
• Jang, S. M., Geng, T., Queenie Li, J.-Y., Xia, R., Huang, C.-T., Kim, H., & Tang, J. (2018). A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis. Computers in Human Behavior, 84, 103–113. doi:10.1016/j.chb.2018.02.032
   Web of Science ®Google Scholar
• Jaramillo-Dent, D., & Pérez-Rodríguez, M. A. (2021). # MigrantCaravan: The border wall and the establishment of otherness on Instagram. New Media & Society, 23(1), 121–141. doi:10.1177/1461444819894241
   Web of Science ®Google Scholar
• Keer, L., & [@leonkeer]. (2019, December 6). Once in Montreal … [Instagram Photo]. Retrieved from https://www.instagram.com/p/B5uqq1zhU1S/?utm_source=ig_web_copy_link
   Google Scholar
• Khattar, D., Goud, J. S., Gupta, M., & Varma, V. (2019, May). Mvae: Multimodal variational autoencoder for fake news detection. In The World Wide Web Conference, CA, San Francisco, USA, (pp. 2915–2921).
   Google Scholar
• Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding echo chambers and filter bubbles: The impact of social media on diversification and partisan shifts in news consumption. MIS Quarterly, 44(4), 1619–1649. doi:10.25300/MISQ/2020/16371
   Web of Science ®Google Scholar
• Krippendorff, K.(2010).Krippendorff’s Alpha. In N. Salkind. (Eds.)Encyclopedia of research design Vols. 1-0. Thousand Oaks, CA: SAGE Publications, Inc, 1–14. doi:10.4135/9781412961288
   Google Scholar
• Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications. doi:10.4135/9781071878781
   Google Scholar
• Lalancette, M., & Raynauld, V. (2019). The power of political image: Justin Trudeau, Instagram, and celebrity politics. American Behavioral Scientist, 63(7), 888–924. doi:10.1177/0002764217744838
   Web of Science ®Google Scholar
• Larsson, A. O. (2018). The news user on social media: A comparative study of interacting with media organizations on Facebook and Instagram. Journalism Studies, 19(15), 2225–2242. doi:10.1080/1461670X.2017.1332957
   Web of Science ®Google Scholar
• Li, X., & Chen, W. (2014). Facebook or Renren? A comparative study of social networking site use and social capital among Chinese international students in the United States. Computers in Human Behavior, 35, 116–123. doi:10.1016/j.chb.2014.02.012
   Web of Science ®Google Scholar
• Li, L., & Yang, Y. (2018). Pragmatic functions of Emoji in internet-based communication a corpus-based study. Asian Pacific Journal of Second and Foreign Language Education, 3(1), 1–12. doi:10.1186/s40862-017-0042-y
   Web of Science ®Google Scholar
• McGregor, S. C., & Molyneux, L. (2020). Twitter’s influence on news judgment: An experiment among journalists. Journalism, 21(5), 597–613. doi:10.1177/1464884918802975
   Web of Science ®Google Scholar
• McKelvey, F., & Dubois, E. (2017). Computational propaganda in Canada: The use of political bots. Computational propaganda research project, Working paper no. 2017.6.
   Google Scholar
• Moskalenko, S., & McCauley, C. (2021). Qanon: Radical opinion versus radical action. Perspectives on Terrorism, 15(2), 142–146.
   Google Scholar
• Neuendorf, K. A. (2016). The content analysis guidebook. California: sage. doi:10.4135/9781071802878
   Google Scholar
• Paul, A., Khattar, P., Kumaraguru, M., Chopra, G., & Chopra, S. Elites Tweet? Characterizing the Twitter Verified User Network. (2019). 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Macao, China. pp. 278–285.
   Google Scholar
• Procházka, O., & Blommaert, J. (2019). Ergoic framing in New Right online groups: Q, the MAGA kid, and the deep state theory. Australian Review of Applied Linguistics, 44(1), 4–36. doi:10.1075/aral.19033.pro
   Web of Science ®Google Scholar
• Rampersad, G., & Althiyabi, T. (2020). Fake news: Acceptance by demographics and culture on social media. Journal of Information Technology & Politics, 17(1), 1–11. doi:10.1080/19331681.2019.1686676
   Web of Science ®Google Scholar
• Ribeiro, M. H., Ottoni, R., West, R., Almeida, V. A., & Meira, W., Jr (2020, January). Auditing radicalization pathways on YouTube. In Proceedings of the 2020 conference on fairness, accountability, and transparency, Barcelona, Spain, (pp. 131–141).
   Google Scholar
• Singh, V. K., Ghosh, I., & Sonagara, D. (2021). Detecting fake news stories via multimodal analysis. Journal of the Association for Information Science and Technology, 72(1), 3–17. doi:10.1002/asi.24359
   Web of Science ®Google Scholar
• Song, C., Ning, N., Zhang, Y., & Wu, B. (2021). A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. Information Processing & Management, 58(1), 102437. doi:10.1016/j.ipm.2020.102437
   Web of Science ®Google Scholar
• So, J., Prestin, A., Lee, L., Wang, Y., Yen, J., & Chou, W. Y. S. (2016). What do people like to “share” about obesity? A content analysis of frequent retweets about obesity on Twitter. Health Communication, 31(2), 193–206. doi:10.1080/10410236.2014.940675
   PubMed Web of Science ®Google Scholar
• Spohr, D. (2017). Fake news and ideological polarization: Filter bubbles and selective exposure on social media. Business Information Review, 34(3), 150–160. doi:10.1177/0266382117722446
   Google Scholar
• Statista. (2022). Most popular social networks worldwide as of January 2022, ranked by number of monthly active users. Retrieved from: https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/
   Google Scholar
• Steffan, D. (2020). Visual self-presentation strategies of political candidates on social media platforms: A comparative study. International Journal of Communication, 14, 23.
   Web of Science ®Google Scholar
• Suntwal, S., Brown, S., & Brandimarte, L. (2021). Pictographs, Ideograms, and Emojis (PIE): A framework for empirical research using non-verbal cues. In Proceedings of the 54th Hawaii International Conference on System Sciences (pp.6400–6409).
   Google Scholar
• Thelwall, M., & Thelwall, S. (2020). A thematic analysis of highly retweeted early COVID-19 tweets: Consensus, information, dissent and lockdown life. Aslib Journal of Information Management, 72(6), 945–962. doi:10.1108/AJIM-05-2020-0134
   Web of Science ®Google Scholar
• Tong, C., Gill, H., Li, J., Valenzuela, S., & Rojas, H. (2020). “Fake news is anything they say!”—Conceptualization and weaponization of fake news among the American public. Mass Communication and Society, 23(5), 755–778. doi:10.1080/15205436.2020.1789661
   Web of Science ®Google Scholar
• Tucker, J. A., Guess, A., Barberá, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., and Nyhan, B. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. California, USA: William and Flora Hewlett Foundation. https://ssrn.com/abstract=3144139
   Google Scholar
• Van Duyn, E., & Collier, J. (2019). Priming and fake news: The effects of elite discourse on evaluations of news media. Mass Communication and Society, 22(1), 29–48. doi:10.1080/15205436.2018.1511807
   Web of Science ®Google Scholar
• Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Chicago Science: Advanced Materials and Devices, 359(6380), 1146–1151. doi:10.1126/science.aap9559
   Google Scholar
• Walters, R. M. (2018). How to tell a fake: Fighting back against fake news on the front lines of social media. Texas Review of Law & Politics, 23(1), 111–179.
   Google Scholar
• Wang, Y., Feng, Y., Hong, Z., Berger, R., & Luo, J. (2017, September). How polarized have we become? A multimodal classification of trump followers and Clinton followers. In International Conference on Social Informatics (pp. 440–456). Springer, Cham.
   Google Scholar
• Wickham-Jones, M. (2020). What did they wish for? Party government, polarization and the American Political Science Association. Journal of American Studies, 54(2). doi:10.1017/S0021875820000018
   Web of Science ®Google Scholar
• Wimmer, R., & Dominick, J. (2013). Mass media research: An introduction. Boston, MA: Cengage Learning.
   Google Scholar
• Yang, J., Rojas, H., Wojcieszak, M., Aalberg, T., Coen, S., Curran, J., Hayashi, K., Iyengar, S., Jones, P.K., Mazzoleni, G., & Papathanassopoulos, S. (2016). Why are “others” so polarized? Perceived political polarization and media use in 10 countries. Journal of Computer-Mediated Communication, 21(5), 349–367. doi:10.1111/jcc4.12166
   Web of Science ®Google Scholar
• Yarchi, M., Baden, C., & Kligler-Vilenchik, N. (2021). Political polarization on the digital sphere: A cross-platform, over-time analysis of interactional, positional, and affective polarization on social media. Political Communication, 38(1–2), 98–139. doi:10.1080/10584609.2020.1785067
   Web of Science ®Google Scholar
• Zhou, X., Mulay, A., Ferrara, E., & Zafarani, R. (2020, October). Recovery: A multimodal repository for COVID-19 news credibility research. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Virtual Event, Ireland, (pp. 3205–3212).
   Google Scholar