The purpose of the present study was to examine the relationships between behavioral factors and fake news sharing and explore the moderating effect of online trust on that relationship. For this, the present study collected data from 352 social media users in South Korea through the survey for which the professional survey company gave electronic gift cards to respondents to increase the response rate and reduce the non-response bias. And, it used hierarchical multiple regression analyses. First, the results show that the more socialization, pass-time, or information-seeking gratification participants perceive online, the more favorable fake news sharing they have. Conversely, entertainment was not significantly associated with fake news sharing in this study. Second, the positive relationship between socialization gratification support and fake news sharing is stronger for participants with high rather than low online trust. However, online trust was found to have no significance on the relationship between other gratification factors and fake news sharing. This study enhances understanding of sharing fake news online, linking negative social media use to fake news sharing. It connects antecedents and variables based on U&G theory and prior research. It also recommends screen time tracking apps with warnings to address social media fatigue and prevent fake news.
AbidAHarriganP (2020) An exploration of social media-enabled voter relationships through uses and gratifications theory, psychological contract and service-dominant orientation. Australasian Marketing Journal28(2): 71–82.
2.
AllcottHGentzkowM (2017) Social media and fake news in the 2016 election. Journal of Economic Perspectives31(2): 211–236.
3.
AnYHuangYDanjumaNU, et al. (2023) Why do people spread fake news? Modelling the factors that influence social media users’ fake news sharing behaviour. Information Development. DOI: 10.1177/02666669231194357.
4.
AnspachNMCarlsonTN (2018) What to believe? Social media commentary and belief in misinformation. Political Behavior42(3): 697–718.
5.
ApukeODOmarB (2020) Fake news proliferation in Nigeria: Consequences, motivations, and prevention through awareness. Humanities & Social Sciences Reviews8(2): 318–327.
6.
ApukeODOmarB (2021) Fake news and COVID-19: Modelling the predictors of fake news sharing among social media users. Telematics and Informatics56: 101475.
7.
BaekKHoltonAHarpD, et al. (2011) The links that bind: Uncovering novel motivations for linking on Facebook. Computers in Human Behavior27(6): 2243–2248.
8.
BalakrishnanV (2022) Socio-demographic predictors for misinformation sharing and authenticating amidst the COVID-19 pandemic among Malaysian young adults. Information Development. DOI: 10.1177/02666669221118922.
9.
BezbaruahSDhirATalwarS, et al. (2021) Believing and acting on fake news related to natural food: The influential role of brand trust and system trust. British Food Journal124(9): 2937–2962.
10.
ChangSELiuAYShenWC (2017) User trust in social networking services: A comparison of Facebook and LinkedIn. Computers in Human Behavior69: 207–217.
ChiuCMHsuMHWangETG (2006) Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems42(3): 1872–1888.
13.
ChoiJ (2016) Why do people use news differently on SNSs? An investigation of the role of motivations, media repertoires, and technology cluster on citizens’ news-related activities. Computers in Human Behavior54: 249–256.
14.
DeweyC (2016) Facebook fake-news writer: ‘I think Donald Trump is in the White House because of me.’ the Washington Post. Available at: https://www.washingtonpost. com/news/the-intersect/wp/2016/11/17/facebook-fakenews-writer-i-think-donaldtrump-is-in-the-white-house-because-of-me/?tid=sm_tw.
15.
DuBoisDLPortilloNRhodesJE, et al. (2011) How effective are mentoring programs for youth? A systematic assessment of the evidence. Psychological Science in the Public Interest12(2): 57–91.
16.
DuffyATandocELingR (2019) Too good to be true, too good not to share: The social utility of fake news. Information Communication and Society23(13): 1965–1979.
17.
EgelhoferJLLechelerS (2019) Fake news as a two-dimensional phenomenon: A framework and research agenda. Annals of the International Communication Association43(2): 97–116.
18.
FangCWengHDaiX, et al. (2016) Topological nodal line semimetals. Chinese Physics B25(11): 117106.
19.
FestingerL (1954) A theory of social comparison processes. Human Relations7(2): 117–140. Fletcher, R., Nielsen.
20.
GeverCV (2021) Why does misinformation about COVID-19 strive on social media platforms?: Suggesting an intervention strategy for Nigerian government. Ianna Journal of Interdisciplinary Studies2(1).
21.
Grabner-KräuterSBitterS (2013) Trust in online social networks: A multifaceted perspective. Forum for Social Economics44(1): 48–68.
22.
GrosserTJLopez-KidwellVLabiancaG (2010) A social network analysis of positive and negative gossip in organizational life.Group & Organization Management35(2): 177–1212.
HaLYoonKZhangX (2013) Consumption and dependency of social network sites as a news medium : A comparison between college students and general population. Journal of Communication and Media Research5(1): 1–14.
25.
HairJFRingleCMSarstedtM (2011) PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice19(2): 139–151.
26.
HairJFRingleCMSarstedtM (2013) Partial least squares structural equation modelling: Rigorous applications, better results and higher acceptance. Long-Range Planning46: 1–12.
27.
HalpernDValenzuelaSKatzJ, et al. (2019) From belief in conspiracy theories to trust in others: Which factors influence exposure. Believing and Sharing Fake News: 217–232.
28.
HarriganPSoutarGChoudhuryMM, et al. (2015) Modelling CRM in a social media age. Australasian Marketing Journal23(1): 27–37.
29.
HouZDuFJiangH, et al. (2020) Assessment of public attention, risk perception, emotional and behavioural responses to the COVID-19 outbreak: Social media surveillance in China. MedRxiv. DOI: 10.1101/2020.03.14.20035956.
30.
HuynhTL (2020) The COVID-19 risk perception: A survey on socioeconomics and media attention. Economics Bulletin40(1): 758–764.
31.
IntroneJGokce YildirimIIandoliL, et al. (2018) How people weave online information into pseudoknowledge. Social Media and Society4(3.
32.
IslamANLaatoSTalukderS, et al. (2020) Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change159: 120201.
33.
JangSMGengTQueenie LiJ-Y, et al. (2018) A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis. Computers in Human Behavior84: 103–113.
34.
KapoorASindwaniRGoelM, et al. (2022) Mobile wallet adoption intention amid COVID-19 pandemic outbreak: A novel conceptual framework. Computers & Industrial Engineering172: 108646.
35.
KrasnovaHSpiekermannSKorolevaK, et al. (2010) Online social networks: Why we disclose. Journal of Information Technology25(2): 109–125.
36.
KarnowskiVLeonhardLKumpelAS (2018) Why users share the news: A theory of reasoned action-based study on the antecedents of news-sharing behavior. Communication Research Reports35(2): 91–100.
37.
KatzEBlumlerJGGurevitchM (1974) The uses and gratifications approach to mass communication. Beverly Hills, Calif: Sage Publications.
38.
KaurPIslamNTandonA, et al. (2021) Social media users’ online subjective wellbeing and fatigue: A network heterogeneity perspective. Technological Forecasting and Social Change172: 121039.
39.
KhanMLIdrisIK (2019) Recognise misinformation and verify before sharing: A reasoned action and information literacy perspective. Behaviour and Information Technology38(1): 1–49.
40.
KimJLeeCEliasT (2015) Factors affecting information sharing in social networking sites amongst university students: Application of the knowledge-sharing model to social networking sites. Online Information Review39(3): 290–309.
41.
KircaburunKJonasonPKGriffithsMD (2018) The dark tetrad traits and problematic social media use: The mediating role of cyberbullying and cyberstalking.Personality and Individual Differences135: 264–269.
42.
KouzyRAbiJJKraitemA, et al. (2020) Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic onTwitter. Cureus12(3): e7255.
43.
KumarAShankarA (2023) Disengagement toward brand-based online communities: The role of culture. Journal of Global Marketing: 1–16.
44.
KumarAShankarABehlA, et al. (2023b) Should I share it? Factors influencing fake news-sharing behaviour: A behavioural reasoning theory perspective. Technological Forecasting and Social Change193: 122647.
45.
KumarAShankarATiwariAK, et al. (2023a) Understanding dark side of online community engagement: An innovation resistance theory perspective. Information Systems and e-Business Management: 1–27.
46.
LamposVMouraSYom-TovE, et al. (2020) Tracking COVID-19 using online search. 93, 4–9.
47.
LeeCSMaL (2012) News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior28(2): 331–339.
48.
LeeCSMaLGohDHL (2011) Why do people share news in social media? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6890 LNCS, 129–140.
49.
LinKYLuHP (2011) Intention to continue using Facebook fan pages from the perspective of social capital theory. Cyberpsychology, Behaviour, and Social Networking14(10): 565–570.
50.
LinSWLiuYC (2012) The effects of motivations, trust, and privacy concern in social networking.Service Business6: 411–424.
51.
LivingstoneSÓlafssonKHelsperEJ, et al. (2017) Maximizing opportunities and minimizing risks for children online: The role of digital skills in emerging strategies of parental mediation. J. Commun67(1): 82–105.
52.
MaLLeeCSGohDH (2013) Understanding news sharing in social media from the diffusion of innovations perspective. Proceedings – 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-IThings-CPSCom2013: 1013–1020.
53.
MacKenzieSBPodsakoffPM (2012) Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing. 88(4): 542–555.
54.
MalikAMahmoodKIslamT (2021) Understanding the Facebook users’ Behavior towards COVID-19 information sharing by integrating the theory of planned behavior and gratifications. Information Development. DOI: 10.1177/02666669211049383.
55.
McAllisterDJ (1995) Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal38(1): 24–59.
56.
MinderPBernsteinA (2011) Social network aggregation using face-recognition. In: CEUR Workshop Proceedings, vol. No. 830. RWTH Aachen (October).
57.
MouJCohenJF (2017) Trust and online consumer health service success: A longitudinal study. Information Development33(2): 169–189.
58.
MustafarajEMetaxasPT (2017) The Fake News Spreading Plague: Was it Preventable? Cornell University Library Retrieved from arXiv:1703.06988 [cs.SI].
59.
NwagwuWEAkintoyeA (2023) Influence of social media on the uptake of emerging musicians and entertainment events. Information Development. DOI: 10.1177/02666669221151162.
60.
O’KeeffeGSClarke-PearsonK (2011) The impact of social media on children, adolescents, and families. Pediatrics127(4): 800–804.
61.
ParkHBlenkinsoppJ (2009) Whistleblowing as planned behavior - A survey of South Korean police officers. Journal of Business Ethics85(4): 545–556.
62.
ParkNChungJELeeS (2012) Explaining the use of text-based communication media: An examination of three theories of media use. Cyberpsychology, Behavior, and Social Networking15(7): 357–363.
63.
PodsakoffPMMacKensieSBLeeJ-Y, et al. (2003) Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
64.
PodsakoffPMOrganD (1986) Self-reports in organizational research: Problems and prospects. Journal of Management12(4): 531–544.
65.
PotthastM.KieselJ.ReinartzK.,et al. (2017). A Stylometric Inquiry into Hyperpartisan and Fake News. arXiv preprint arXiv:1702.05638.
66.
PulidoCMVillarejo-CarballidoBRedondo-SamaG, et al. (2020) COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information. International Sociology35(4): 377–392.
67.
RawazA (2016) Enhancing Customer Loyalty in Health Clubs: The Role of Social Media (Doctoral dissertation, Victoria University).
68.
RubinVLChenYConroyNJ (2015) Decpetion detection for news: Three types of fake news. Proceedings of the Association for Information Science and Technology52(1): 1–4.
69.
SahooSRGuptaBB (2021) Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing100: 106983.
70.
ShadeLRSinghR (2016) “Honestly, we’re not spying on kids”: School surveillance of young people’s social media. Social Media + Society2(4). DOI: 10.1177/2056305116680005.
71.
ShankarAJebarajakirthyCNayalP, et al. (2022) Online food delivery: A systematic synthesis of literature and a framework development. International Journal of Hospitality Management104: 103240.
72.
ShankarAKumarAYadavR, et al. (2023) Should I adopt an integrated virtual clinic? A dual-factor theory perspective. Journal of Consumer Behaviour. DOI:https://doi.org/10.1002/cb.2202.
73.
ShinJJianLDriscollK, et al. (2018) The diffusion of misinformation on SM: Temporal pattern, message, and source. Computers in Human Behavior83: 278–287.
74.
ShuKSlivaAWangS, et al. (2017) Fake news detection on SM. ACM SIGKDD Explor. Newslett19(1): 22–36.
75.
SihombingSO (2017) Predicting intention to share news through social media: An empirical analysis in Indonesian youth context. Business and Economic Horizons13(4): 468–477.
TalwarSDhirASinghD, et al. (2020) Sharing of fake news on social media: Application of the honeycomb framework and the third-person effect hypothesis. Journal of Retailing and Consumer Services57: 102197.
78.
TandonAKaurPBhattY, et al. (2021) Why do people purchase from food delivery apps? A consumer value perspective. Journal of Retailing and Consumer Services63: 102667.
79.
ThompsonNWangXDayaP (2019) Determinants of news sharing behavior on social media. Journal of Computer Information Systems60(6): 593–601.
80.
TourangeauRRipsLJRasinskiK (2000) The Psychology of Survey Response. Cambridge, U.K.: Cambridge University Press.
81.
VicarioMBessiAZolloF, et al. (2016) The spreading of misinformation online. Proceedings of the National Academy of Sciences of the United States of America113(3): 554–559.
82.
WassermanHMadrid-MoralesD (2019) An exploratory study of “fake news” and media trust in Kenya, Nigeria and South Africa. African Journalism Studies3670: 1–17.
83.
WaszakPMKasprzycka-WaszakWKubanekA, et al. (2018) The spread of medical fake news in social media–the pilot quantitative study.Health Policy and Technology7(2): 115–118.
84.
ZahraiKVeerEBallantinePW, et al. (2021) Conceptualising selfcontrol on problematic social media use. Australasian Marketing Journal30(1): 74–89.
