Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective

A K M Najmul Islam, Samuli Laato, Shamim Talukder, Erkki Sutinen, A K M Najmul Islam, Samuli Laato, Shamim Talukder, Erkki Sutinen

Abstract

Social media plays a significant role during pandemics such as COVID-19, as it enables people to share news as well as personal experiences and viewpoints with one another in real-time, globally. Building off the affordance lens and cognitive load theory, we investigate how motivational factors and personal attributes influence social media fatigue and the sharing of unverified information during the COVID-19 pandemic. Accordingly, we develop a model which we analyse using the structural equation modelling and neural network techniques with data collected from young adults in Bangladesh (N = 433). The results show that people, who are driven by self-promotion and entertainment, and those suffering from deficient self-regulation, are more likely to share unverified information. Exploration and religiosity correlated negatively with the sharing of unverified information. However, exploration also increased social media fatigue. Our findings indicate that the different use purposes of social media introduce problematic consequences, in particular, increased misinformation sharing.

Keywords: COVID-19; Fake news; Fatigue; Misinformation; Pandemic; Social media.

© 2020 Elsevier Inc. All rights reserved.

Figures

Fig. 1
Fig. 1
The proposed research model.
Fig. 2
Fig. 2
PLS analysis results (***p < 0.001; **p < 0.01; *p < 0.05).
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/7354273/bin/gr3_lrg.jpg
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/7354273/bin/gr4_lrg.jpg

References

    1. Aaronson L.S., Teel C.S., Cassmeyer V., Neuberger G.B., Pallikkathayil L., Pierce J.…Wingate A. Defining and measuring fatigue. Image J. Nurs. Scholarsh. 1999;31(1):45–50.
    1. Ahmed W., Vidal-Alaball J., Downing J., Seguí F.L. COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data. J. Med. Internet Res. 2020;22(5):e19458.
    1. Allcott H., Gentzkow M. Social media and fake news in the 2016 election. J. Econ. Perspect. 2017;31(2):211–236.
    1. Barberá P., Tucker J.A., Guess A., Vaccari C., Siegel A., Sanovich S.…Nyhan B. William + Flora Hewlett Foundation; California, USA: 2018. Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature.
    1. Barbieri, T., Basso, G., & Scicchitano, S. (2020). Italian Workers at Risk During the Covid-19 Epidemic. Available at SSRN 3572065.
    1. Bardo M.T., Donohew R.L., Harrington N.G. Psychobiology of novelty seeking and drug seeking behavior. Behav. Brain Res. 1996;77(1–2):23–43.
    1. Bright L.F., Kleiser S.B., Grau S.L. Too much Facebook? An exploratory examination of social media fatigue. Comput. Hum. Behav. 2015;44:148–155.
    1. Brooke J., Jackson D. Older people and COVID-19: isolation, risk and ageism. J. Clin. Nurs. 2020;29(13–14):2044–2046.
    1. Campbell H.A., editor. Digital Religion: Understanding Religious Practice in New Media Worlds. Routledge; 2012.
    1. Cao X., Khan A.N., Zaigham G.H., Khan N.A. The stimulators of social media fatigue among students: role of moral disengagement. J. Educ. Comput. Res. 2019;57(5):1083–1107.
    1. Chadwick, A., & Vaccari, C. (2019). News Sharing on UK Social Media: Misinformation, Disinformation, and Correction.
    1. Chan F.T., Chong A.Y. A SEM–neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decis. Support Syst. 2012;54(1):621–630.
    1. Chen X., Wei S., Davison R.M., Rice R.E. How do enterprise social media affordances affect social network ties and job performance? Inf. Technol. People. 2019;33(1):361–388.
    1. Chiodo C.P., Broughton K.K., Michalski M.P. Caution: wit and humor during the COVID-19 pandemic. Foot Ankle Int. 2020 1071100720923651.
    1. Chong A.Y.-L. A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Syst. Appl. 2013;40(4):1240–1247.
    1. Coibion O., Gorodnichenko Y., Weber M. National Bureau of Economic Research; 2020. Labor Markets During the COVID-19 Crisis: A Preliminary View (No. w27017)
    1. de Zúñiga H.G., Barnidge M., Scherman A. Social media social capital, offline social capital, and citizenship: exploring asymmetrical social capital effects. Political Commun. 2017;34(1):44–68.
    1. Del Vicario M., Bessi A., Zollo F., Petroni F., Scala A., Caldarelli G.…Quattrociocchi W. The spreading of misinformation online. Proc. Natl. Acad. Sci. 2016;113(3):554–559.
    1. Dhir A., Yossatorn Y., Kaur P., Chen S. Online social media fatigue and psychological wellbeing—A study of compulsive use, fear of missing out, fatigue, anxiety and depression. Int. J. Inf. Manag. 2018;40:141–152.
    1. Dhir A., Kaur P., Chen S., Pallesen S. Antecedents and consequences of social media fatigue. Int. J. Inf. Manag. 2019;48:193–202. .4.
    1. Dulawa S.C., Grandy D.K., Low M.J., Paulus M.P., Geyer M.A. Dopamine D4 receptor-knock-out mice exhibit reduced exploration of novel stimuli. J. Neurosci. 1999;19(21):9550–9556.
    1. Farooq A., Laato S., Islam A.K.M.N. Impact of online information on self-isolation intention during the COVID-19 pandemic: cross-sectional study. J. Med. Internet Res. 2020;22(5):e19128.
    1. Figueira Á., Oliveira L. The current state of fake news: challenges and opportunities. Procedia Comput. Sci. 2017;121:817–825.
    1. Fornell C., Larker D. Structural equation modeling and regression: guidelines for research practice. J. Market. Res. 1981;18(1):39–50.
    1. Gibson J.J. Houghton Mifflin; Oxford, England: 1966. The Senses Considered As Perceptual Systems.
    1. Gibson J.J. The theory of affordances. In: Shaw R., Bransford J., editors. Perceiving, Acting, and Knowing: Toward an Ecological Psychology. Lawrence Erlbaum; Hillsdale, NJ: 1977. pp. 67–82.
    1. Guerra P.C., Meira W., Jr, Cardie C., Kleinberg R. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. 2013. A measure of polarization on social media networks based on community boundaries.
    1. Hair J.F., Black W.C., Babin B.J., Anderson R.E. 7th ed. Prentice Hall; 2010. Multivariate Data Analysis.
    1. Hitchens C. McClelland & Stewart; 2008. God Is Not Great: How Religion Poisons Everything.
    1. Hollender N., Hofmann C., Deneke M., Schmitz B. Integrating cognitive load theory and concepts of human–computer interaction. Comput. Hum. Behav. 2010;26(6):1278–1288.
    1. Howard, P.N., Bolsover, G., Kollanyi, B., Bradshaw, S., & Neudert, L.M. (2017). Junk News and Bots During the US Election: What Were Michigan Voters Sharing Over Twitter. CompProp, OII, Data Memo.
    1. Huang Y.L., Starbird K., Orand M., Stanek S.A., Pedersen H.T. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. 2015. Connected through crisis: emotional proximity and the spread of misinformation online; pp. 969–980.
    1. Islam A.K.M.N., Whelan E., Brooks S. Social media overload and fatigue: the moderating role of multitasking computer self-efficacy. Proceedings of the Twenty-Fourth Americas Conference on Information Systems; New Orleans; 2018.
    1. Islam A.K.M.N., Mäntymäki M., Benbasat I. Duality of self-promotion on social networking sites. Inf. Technol. People. 2019;32(2):269–296.
    1. Islam M.N., Islam A.N. A systematic review of the digital interventions for fighting COVID-19: the Bangladesh perspective. IEEE Access. 2020;8:114078–114087.
    1. Kashdan T.B., Rose P., Fincham F.D. Curiosity and exploration: facilitating positive subjective experiences and personal growth opportunities. J. Personal. Assess. 2004;82(3):291–305.
    1. Khan M.L., Idris I.K. Recognise misinformation and verify before sharing: a reasoned action and information literacy perspective. Behav. Inf. Technol. 2019;38(12):1194–1212.
    1. Khayer A., Talukder M.S., Bao Y., Hossain M.N. Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: a dual-stage analytical approach. Technol. Soc. 2020;60
    1. Kietzmann J.H., Hermkens K., McCarthy I.P., Silvestre B.S. Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horiz. 2011;54(3):241–251.
    1. Kim A., Dennis A.R. Says who? The effects of presentation format and source rating on fake news in social media. MIS Q. 2019;43(3)
    1. Kim A., Moravec P.L., Dennis A.R. Combating fake news on social media with source ratings: the effects of user and expert reputation ratings. J. Manag. Inf. Syst. 2019;36(3):931–968.
    1. Kouzy R., Abi Jaoude J., Kraitem A., El Alam M.B., Karam B., Adib E.…Baddour K. Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on twitter. Cureus. 2020;12(3)
    1. Ku K.Y., Kong Q., Song Y., Deng L., Kang Y., Hu A. What predicts adolescents’ critical thinking about real-life news? The roles of social media news consumption and news media literacy. Think. Skills Creat. 2019;33
    1. Laato S., Islam A.K.M.N., Islam M.N., Whelan E. What drives unverified information sharing and cyberchondria during the COVID-19 pandemic? Eur. J. Inf. Syst. 2020:1–18.
    1. Laato S., Islam A.N., Laine T.H. Did location-based games motivate players to socialize during COVID-19? Telemat. Inform. 2020
    1. LaRose R., Lin C.A., Eastin M.S. Unregulated Internet usage: addiction, habit, or deficient self-regulation? Media Psychol. 2003;5(3):225–253.
    1. Lau W.W. Effects of social media usage and social media multitasking on the academic performance of university students. Comput. Hum. Behav. 2017;68:286–291.
    1. Lee K.C., Perry S.D. Student instant message use in a ubiquitous computing environment: effects of deficient self-regulation. J. Broadcast. Electron Media. 2004;48(3):399–420.
    1. Lee C.S., Ma L. News sharing in social media: the effect of gratifications and prior experience. Comput. Hum. Behav. 2012;28(2):331–339.
    1. Lee A.R., Son S.M., Kim K.K. Information and communication technology overload and social networking service fatigue: a stress perspective. Comput. Hum. Behav. 2016;55:51–61.
    1. Lowry P.B., Gaskin J. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: when to choose it and how to use it. IEEE Trans. Prof. Commun. 2014;57(2):123–146.
    1. Liang H., Saraf N., Hu Q., Xue Y. Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Q. 2007;31(1):59–87.
    1. Mahase E. Covid-19: six million doses of hydroxychloroquine donated to US despite lack of evidence. BMJ. 2020;368:m1166. doi: 10.1136/bmj.m1166.
    1. McGrath A.E. John Wiley & Sons; 2013. Dawkins' God: Genes, Memes, and the Meaning of Life.
    1. Mele, N., Lazer, D., Baum, M., Grinberg, N., Friedland, L., Joseph, K., … & Mattsson, C. (2017). Combating Fake News: An Agenda For Research and Action. [ONLINE] available at , checked April 28th, 2020.
    1. Misra S., Stokols D. Psychological and health outcomes of perceived information overload. Environ. Behav. 2012;44(6):737–759.
    1. Moon J.H., Lee E., Lee J.A., Choi T.R., Sung Y. The role of narcissism in self-promotion on Instagram. Personal. Individ. Differ. 2016;101:22–25.
    1. Mutlu-Bayraktar D., Cosgun V., Altan T. Cognitive load in multimedia learning environments: a systematic review. Comput. Educ. 2019;141
    1. Mäntymäki M., Islam A.N. The Janus face of Facebook: positive and negative sides of social networking site use. Comput. Hum. Behav. 2016;61:14–26.
    1. Nekmat E. Nudge effect of fact-check alerts: source influence and media skepticism on sharing of news misinformation in social media. Soc. Media Soc. 2020;6(1) 2056305119897322.
    1. Nesi J., Prinstein M.J. Using social media for social comparison and feedback-seeking: gender and popularity moderate associations with depressive symptoms. J. Abnorm. Child Psychol. 2015;43(8):1427–1438.
    1. Norman D.A. Basic Books; New York, NY: 1988. The Psychology of Everyday Things.
    1. O'brien R.M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 2007;41(5):673–690.
    1. Ooi K.-.B., Lee V.-.H., Tan G.W.-H., Hew T.-.S., Hew J.-.J. Cloud computing in manufacturing: the next industrial revolution in Malaysia? Expert Syst. Appl. 2018;93:376–394.
    1. Suler J. The online disinhibition effect. Cyberpsychol. Behav. 2004;7(3):321–326.
    1. Paas F., Tuovinen J.E., Tabbers H., Van Gerven P.W. Cognitive load measurement as a means to advance cognitive load theory. Educ. Psychol. 2003;38(1):63–71.
    1. Panksepp J. Cross-species neuroaffective parsing of primal emotional desires and aversions in mammals. Emot. Rev. 2013;5(3):235–240.
    1. Pennycook G., Rand D.G. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc. Natl. Acad. Sci. 2019;116(7):2521–2526.
    1. Pentina I., Tarafdar M. From “information” to “knowing”: exploring the role of social media in contemporary news consumption. Comput. Hum. Behav. 2014;35:211–223.
    1. Piper B.F., Lindsey A.M., Dodd M.J., Ferketich S., Paul S.M., Weller S. Proceedings of the Management of Pain, Fatigue and Nausea. Macmillan Education UK; 1989. The development of an instrument to measure the subjective dimension of fatigue; pp. 199–208.
    1. Podsakoff P.M., MacKenzie S.B., Lee J.-.Y., Podsakoff N.P. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 2003;88(5):879.
    1. Harman H.H. University of Chicago Press; 1976. Modern Factor Analysis.
    1. Khalaf D.R., Hlais S.A., Haddad R.S., Mansour C.M., Pelissolo A.J., Naja W.J. Developing and testing an original Arabic religiosity scale. Middle East Curr. Psychiatry. 2014;21(2):127–138.
    1. Quan-Haase A., Young A.L. Uses and gratifications of social media: a comparison of Facebook and instant messaging. Bull. Sci. Technol. Soc. 2010;30(5):350–361.
    1. Rieger D., Klimmt C. The daily dose of digital inspiration 2: themes and affective user responses to meaningful memes in social media. New Media Soc. 2019;21(10):2201–2221.
    1. Shabani K., Khatib M., Ebadi S. Vygotsky's zone of proximal development: instructional implications and teachers' professional development. Engl. Lang. Teach. 2010;3(4):237–248.
    1. Sharma S.K., Al-Badi A.H., Govindaluri S.M., Al-Kharusi M.H. Predicting motivators of cloud computing adoption: a developing country perspective. Comput. Hum. Behav. 2016;62:61–69.
    1. Spohr D. Fake news and ideological polarization: filter bubbles and selective exposure on social media. Bus. Inf. Rev. 2017;34(3):150–160.
    1. Sweller J. Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ. Psychol. Rev. 2010;22(2):123–138.
    1. Sweller J. vol. 55. Academic Press; 2011. Cognitive load theory; pp. 37–76. (Psychology of Learning and Motivation).

    2. https://www.sciencedirect.com/science/article/pii/B9780123876911000028

    1. Talukder M.S., Sorwar G., Bao Y., Ahmed J.U., Palash M.A.S. Predicting antecedents of wearable healthcare technology acceptance by elderly: a combined SEM-Neural Network approach. Technol. Forecast. Soc. Change. 2020;150
    1. Talwar S., Dhir A., Kaur P., Zafar N., Alrasheedy M. Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. J. Retail. Consum. Serv. 2019;51:72–82.
    1. Tan G.W.-H., Ooi K.-.B., Leong L.-.Y., Lin B. Predicting the drivers of behavioral intention to use mobile learning: a hybrid SEM-neural networks approach. Comput. Hum. Behav. 2014;36:198–213.
    1. Thompson N., Wang X., Daya P. Determinants of news sharing behavior on social media. J. Comput. Inf. Syst. 2019:1–9.
    1. Turel O., Serenko A. The benefits and dangers of enjoyment with social networking websites. Eur. J. Inf. Syst. 2012;21(5):512–528.
    1. Vaezi A., Javanmard S.H. Infodemic and risk communication in the era of CoV-19. Adv. Biomed. Res. 2020;9
    1. Vicario M.D., Quattrociocchi W., Scala A., Zollo F. Polarization and fake news: early warning of potential misinformation targets. ACM Trans. Web. 2019;13(2):1–22.
    1. Vogel E.A., Rose J.P., Roberts L.R., Eckles K. Social comparison, social media, and self-esteem. Psychol. Pop. Media Cult. 2014;3(4):206.
    1. Vogel E.A., Rose J.P. Self-reflection and interpersonal connection: making the most of self-presentation on social media. Trans. Issues Psychol. Sci. 2016;2(3):294.
    1. Vuori V., Okkonen J. Knowledge sharing motivational factors of using an intraorganizational social media platform. J. Knowl. Manag. 2012;16(4):592–603.
    1. Wang P., Angarita R., Renna I. Proceedings of the Companion Proceedings of the Web Conference 2018. 2018, April. Is this the era of misinformation yet: combining social bots and fake news to deceive the masses; pp. 1557–1561.
    1. Whelan E., Islam A.N., Brooks S. Applying the SOBC paradigm to explain how social media overload affects academic performance. Comput. Educ. 2020;143
    1. Whelan E., Islam A.N., Brooks S. Is boredom proneness related to social media overload and fatigue? A stress–strain–outcome approach. Internet Res. 2020;30(3):869–887.
    1. White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econom. J. Econom. Soc. 1980:817–838.
    1. Wong C.-.H., Tan G.W.-H., Hew T.-.S., Ooi K.-.B. Can mobile TV be a new revolution in the television industry? Comput. Hum. Behav. 2016;55:764–776.
    1. Xiao L., Mou J. Social media fatigue-Technological antecedents and the moderating roles of personality traits: the case of WeChat. Comput. Hum. Behav. 2019;101:297–310.
    1. Zarocostas J. How to fight an infodemic. Lancet. 2020;395(10225):676.
    1. Zhou T. Understanding online community user participation: a social influence perspective. Internet Res. Electron. Netw. Appl. Policy. 2011;21(1):67–81.

Source: PubMed

Sponsors en medewerkers

Medische omstandigheden

Geneesmiddelinterventies

3
Abonneren