Journal of Intelligent Communication

Article

Fake News on Social Media: Predicting Which Media Format Influences Fake News Most on Facebook

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https://doi.org/10.54963/jic.v2i1.56
Demuyakor, J., & Opata, E. M. (2022). Fake News on Social Media: Predicting Which Media Format Influences Fake News Most on Facebook. Journal of Intelligent Communication, 1(2). https://doi.org/10.54963/jic.v2i1.56
Volume 1 Issue 2: September 2022
Copyright (c) 2022 Author(s)
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Authors

  • John Demuyakor
    Institute of Communication Studies, Communication University of China, Beijing, China
  • Edward Martey Opata Department of Television, Communication University of China, Beijing, China

Video and audio fake news formats distributed on Facebook in the form of dis/misinformation have caused so much fear, and panic, and received unpleasant reactions among users. This study adopted a comparative experimental design to test the hypotheses by varying the valence, thus types of formats or modalities, the educational levels, credibility, and sharing intentions of the participants, thus, 2 (video vs audio formats) ×2 (credibility vs sharing intentions) × 2 (literate vs. illiterate) to predict the specific media format(s) that influence the spread of fake news on Facebook among users in Ghana. The participants (N = 340) for this study were recruited from the three municipalities in Accra the national capital of Ghana. The research design regarding data sources was carried out in the form of a street intercept survey method. The findings show that users of Facebook believed and shared video formatted fake news than audio formatted. Further analyses indicate that the educational levels (literacy and illiteracy) variables that influence the spread of fake news on Facebook were not consistent. Besides, an in-depth theoretical explanation for modality effects when viewed from the perspective of mobile media, the study has proposed some theoretical and practical implications.

Keywords:

Fake news Facebook Video formats Audio format Misinformation Disinformation

References

  1. Jalilvand, M.R., 2017. Word-of-mouth vs. mass media: Their contributions to destination image formation. Anatolia. 28(2), 151-162. DOI: https://doi.org/10.1080/13032917.2016.1270840
  2. Appiah, O., 2006. Rich media, poor media: The impact of audio/video vs. text/picture testimonial ads on browsers’evaluations of commercial websites and online products. Journal of Current Issues & Research in Advertising. 28(1), 73-86. DOI: https://doi.org/10.1080/10641734.2006.10505192
  3. Demuyakor, J., Nyatuame, I.N., Obiri, S., 2021. Unmasking covid-19 vaccine “infodemic” in the social media. Online Journal of Communication and Media Technologies. 11(4), e202119.DOI: https://doi.org/10.30935/ojcmt/11200
  4. Loomba, S., de Figueiredo, A., Piatek, S.J., et al., 2021. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour. 5(3), 337-348. DOI: https://doi.org/10.1038/s41562-021-01056-1
  5. WHO, UN, UNICEF, UNDP, UNESCO, UNAIDS, ITU, UN Global Pulse, and IFRC, 2020. Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation. https://www.who.int/ news/item/23-09-2020-managing-the-covid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformation.
  6. Islam, M.S., Sarkar, T., Khan, S.H., et al., 2020. COVID-19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis. The American Journal of Tropical Medicine and Hygiene. 103(4), 1621-1629. DOI: https://doi.org/10.4269/ajtmh.20-0812
  7. Egelhofer, J.L., Lecheler, S., 2019. Fake news as a two-dimensional phenomenon: A framework and research agenda. Annals of the International Communication Association. 43(2), 97-116. DOI: https://doi.org/10.1080/23808985.2019.1602782
  8. Vilaro, M.J., Wilson-Howard, D.S., Griffin, L.N., et al., 2020. Tailoring virtual human delivered interventions: A digital intervention promoting colorectal cancer screening for Black women. Psycho-Oncology. 29(12), 2048-2056. DOI: https://doi.org/10.1002/pon.5538
  9. Huskey, R., Wilcox, S., Clayton, R.B., et al., 2020. The limited capacity model of motivated mediated message processing: Meta-analytically summarizing two decades of research. Annals of the International Communication Association. 44(4), 322-349. DOI: https://doi.org/10.1080/23808985.2020.1839939
  10. Fisher, J.T., Weber, R., 2020. Limited capacity model of motivated mediated message processing (LC4MP). In Van Den Bulck, J., (Ed.) International Encyclopedia of Media Psychology. Hoboken, NJ: Wiley Blackwell.
  11. Lang, A., 2006. Using the limited capacity model of motivated mediated message processing to design effective cancer communication messages. Journal of Communication. 56(suppl_1), S57-S80. DOI: https://doi.org/10.1111/j.1460-2466.2006.00283.x
  12. Niether, D., Wiegand, S., 2017. A heuristic approach to understanding the accumulation process in hydrothermal pores. Entropy. 19(1), 33. DOI: https://doi.org/10.3390/e19010033
  13. Kumkale, G.T., Albarracín, D., Seignourel, P.J., 2010. The effects of source credibility in the presence or absence of prior attitudes: Implications for the design of persuasive communication campaigns: source credibility and persuasion. Journal of Applied Social Psychology. 40(6), 1325-1356. DOI: https://doi.org/10.1111/j.1559-1816.2010.00620.x
  14. Chaiken, S., Liberman, A., Eagly, A.H., 1989. Heuristic and Systematic Information Processing within and beyond the Persuasion Context. In J. S. Uleman, & J. A. Bargh (Eds.), Unintended Thought. Guilford. pp. 212-252.
  15. Kruglanski, A., Thompson, E., 1999. Persuasion by a Single Route: A View from the Unimodel. Psychological Inquiry. 10(2), 83-109. Retrieved from https:// www.jstor.org/stable/1449223.
  16. Mitchell, M.B., Cimino, C.R., Benitez, A., et al., 2012. Cognitively stimulating activities: Effects on cognition across four studies with up to 21 years of longitudinal data. Journal of Aging Research. pp. 1-12. DOI: https://doi.org/10.1155/2012/461592
  17. Takada, M., Fukui, Y., Matsuura, Y., et al., 2015. Peripheral viewing during exposure to a 2D/3D video clip: Effects on the human body. Environmental Health and Preventive Medicine. 20(2), 79-89. DOI: https://doi.org/10.1007/s12199-014-0424-4
  18. Chung, S., Sparks, J.V., 2016. Motivated processing of peripheral advertising information in video games. Communication Research. 43(4), 518-541. DOI: https://doi.org/10.1177/0093650214566623
  19. Castro-Alonso, J.C., Sweller, J., 2020. The modality effect of cognitive load theory. In W. Karwowski, T. Ahram, & S. Nazir (Eds.). Advances in Human Factors in Training, Education, and Learning Sciences. 963, 75-84. Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-20135-7_7
  20. Wieland, M., Kleinen-von Königslöw, K., 2020. Conceptualizing different forms of news processing following incidental news contact: A triple-path model. Journalism. 21(8), 1049-1066. DOI: https://doi.org/10.1177/1464884920915353
  21. Sherman, L.E., Michikyan, M., Greenfield, P.M., 2013. The effects of text, audio, video, and in-person communication on bonding between friends. Cyberpsychology: Journal of Psychosocial Research on Cyberspace. 7(2). DOI: https://doi.org/10.5817/CP2013-2-3
  22. Vaccari, C., Chadwick, A., 2020. Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society. 6(1), 205630512090340. DOI: https://doi.org/10.1177/2056305120903408
  23. Deane, P., 2020. Building and justifying interpretations of texts: A key practice in the English language arts. ETS Research Report Series. (1), 1-53. DOI: https://doi.org/10.1002/ets2.12304
  24. Hall, A., 2012. Perceived realism. Oxford University Press. pp. 9780199756841-0046. DOI: https://doi.org/10.1093/obo/9780199756841-0046
  25. Chen, K., Luo, Y., Hu, A., et al., 2021. Characteristics of misinformation spreading on social media during the covid-19 outbreak in china: A descriptive analysis. Risk Management and Healthcare Policy. 14, 1869-1879. DOI: https://doi.org/10.2147/RMHP.S312327
  26. Xian, J., Yang, D., Pan, L., et al., 2019. Misinformation spreading on correlated multiplex networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 29(11), 113123. DOI: https://doi.org/10.1063/1.5121394
  27. Dubois, E., Minaeian, S., Paquet-Labelle, A., et al., 2020. Who to trust on social media: How opinion leaders and seekers avoid misinformation and echo chambers. Social Media + Society. 6(2), 205630512091399. DOI: https://doi.org/10.1177/2056305120913993
  28. Winter, S., Neubaum, G., 2016. Examining characteristics of opinion leaders in social media: A motivational approach. Social Media + Society. 2(3), 205630511666585. DOI: https://doi.org/10.1177/2056305116665858
  29. McPhetres, J., Rand, D.G., Pennycook, G., 2021. Character deprecation in fake news: Is it in supply or demand? Group Processes & Intergroup Relations. 24(4), 624-637. DOI: https://doi.org/10.1177/1368430220965709
  30. Peeters, W., 2019. The peer interaction process on Facebook: A social network analysis of learners’ online conversations. Education and Information Technologies. 24(5), 3177-3204. DOI: https://doi.org/10.1007/s10639-019-09914-2
  31. Young, L.E., Soliz, S., Xu, J.J., et al., 2020. A review of social media analytic tools and their applications to evaluate activity and engagement in online sexual health interventions. Preventive Medicine Reports. 19, 101158. DOI: https://doi.org/10.1016/j.pmedr.2020.101158
  32. Buchanan, T., 2020. Why do people spread false information online? The effects of message and viewer characteristics on the self-reported likelihood of sharing social media disinformation. PLoS One. 15(10), e0239666. DOI: https://doi.org/10.1371/journal.pone.0239666
  33. Pop, M.I., Ene, I., 2019. Influence of the educational level on the spreading of Fake News regarding the energy field in the online environment. Proceedings of the International Conference on Business Excellence. 13(1), 1108-1117. DOI: https://doi.org/10.2478/picbe-2019-0097
  34. Radwan, E., Radwan, A., Radwan, W., 2020. The role of social media in spreading panic among primary and secondary school students during the COVID-19 pandemic: An online questionnaire study from the Gaza Strip, Palestine. Heliyon. 6(12), e05807. DOI: https://doi.org/10.1016/j.heliyon.2020.e05807
  35. Guess, A.M., Lerner, M., Lyons, B., et al., 2020. A digital media literacy intervention increases discernment between mainstream and false news in the United States and India. Proceedings of the National Academy of Sciences. 117(27), 15536-15545. DOI: https://doi.org/10.1073/pnas.1920498117
  36. Rafique, N., Ishaq, A., Shoaib, M., et al., 2020. Uses and impact of social media on work performance of low literate people. R. R. Chillarige, S. Distefano, & S. S. Rawat (Eds.), Advances in Computational Intelligence and Informatics. Springer Singapore. 119, 381-387. DOI: https://doi.org/10.1007/978-981-15-3338-9_43
  37. Appel, G., Grewal, L., Hadi, R., et al., 2020. The future of social media in marketing. Journal of the Academy of Marketing Science. 48(1), 79-95. DOI: https://doi.org/10.1007/s11747-019-00695-1
  38. Grassini, S., Laumann, K., 2020. Questionnaire measures and physiological correlates of presence: A systematic review. Frontiers in Psychology. 11, 349. DOI: https://doi.org/10.3389/fpsyg.2020.00349
  39. Tiffen, R., Jones, P.K., Rowe, D., et al., 2014. Sources in the News: A comparative study. Journalism Studies. 15(4), 374-391. DOI: https://doi.org/10.1080/1461670X.2013.831239
  40. Nasah, A., DaCosta, B., Kinsell, C., et al., 2010. The digital literacy debate: An investigation of digital propensity and information and communication technology. Educational Technology Research and Development. 58(5), 531-555. Retrieved from http:// www.jstor.org/stable/40929417.
  41. Ryan, G., Sfar-Gandoura, H., 2018. Disseminating research information through Facebook and Twitter (Drift): Presenting an evidence-based framework. Nurse Researcher. 26(1), 41-49. DOI: https://doi.org/10.7748/nr.2018.e1562
  42. Quinn, K., 2018. Cognitive effects of social media use A case of older adults. Social Media + Society. 4(3), 205630511878720. DOI: https://doi.org/10.1177/2056305118787203
  43. Lee, B., McLeod, D.M., 2020. Reconceptualizing cognitive media effects theory and research under the judged usability model. Review of Communication Research. 8, 17-50. Retrieved from https://www. rcommunicationr.org/index.php/rcr/article/view/65.
  44. Luttrell, A., 2018. Dual-process models of persuasion. A. Luttrell, Oxford Research Encyclopedia of Psychology. Oxford University Press. DOI: https://doi.org/10.1093/acrefore/97801902365 57.013.319
  45. Li, S., Ding, Q., Yuan, Y., et al., 2021. Audio-visual causality and stimulus reliability affect audio-visual synchrony perception. Frontiers in Psychology. 12, 629996. DOI: https://doi.org/10.3389/fpsyg.2021.629996
  46. Connor Desai, S., Reimers, S., 2019. Comparing the use of open and closed questions for Web-based measures of the continued-influence effect. Behavior Research Methods. 51(3), 1426-1440. DOI: https://doi.org/10.3758/s13428-018-1066-z
  47. Dunbar, N.E., Connelly, S., Jensen, M.L., et al., 2014. Fear appeals, message processing cues, and credibility in the websites of violent, ideological, and nonideological groups. Journal of Computer-Mediated Communication. 19(4), 871-889. DOI: https://doi.org/10.1111/jcc4.12083
  48. Fransen, M.L., Fennis, B.M., Pruyn, A.Th.H., 2010. Matching communication modalities: The effects of modality congruence and processing style on brand evaluation and brand choice. Communication Research. 37(4), 576-598. DOI: https://doi.org/10.1177/0093650210368251
  49. Krumsvik, A.H., 2018. Redefining user involvement in digital news media. Journalism Practice. 12(1), 19- 31. DOI: https://doi.org/10.1080/17512786.2017.1279025
  50. Wang, W., Ma, Y., Wu, T., et al., 2019. Containing misinformation spreading in temporal social networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 29(12), 123131. DOI: https://doi.org/10.1063/1.5114853