TARU PUBLICATIONS
 Journal of Statistics and Management Systems cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

Factors affecting rumor spreading of social network sites

*

* Corresponding author · click or hover a name for details

pp. 801–821Vol. 26Issue 4May 2023DOI: 10.47974/JSMS-927XML
Received:
31 Dec 2020
Accepted:
31 Dec 2021
Published Online:
10 Aug 2023
Article type:
Research Article
Language:
EN
Article no.:
JSMS-927
Pages:
801–821

Abstract

The emergence of social network sites (e.g. Facebook, Twitter, and YouTube etc.) has been playing an important role for modern people’s lives. The social network sites provide users online maintaining and expanding interpersonal space and opportunities. Many social network sites have been a platform to share the news and information with friends and family. The social network sites facilitate the information flow through user’s friend circle, so various information spreads quickly and so does rumor. This study adopts the theoretical perspective of an Elaboration Likelihood Model to identify the informative and normative factors that influence the credibility judgment of online rumors in social network sites context. Based on the 335 valid samples from the online questionnaire, statistical testing of research models using partial least squares. The results of this study are similar to the ELM claim that argument quality and source credibility have significant effects on rumor credibility. This indicates that rumor spreading of SNSs is based not only on the argument quality, but also on the source credibility. Moreover, compared with argument quality, the source credibility has a stronger impact on rumor credibility. This means that the degree to which the user needs source credibility plays a major key role to decide the rumor credibility. The study also suggests the possible importance of argument quality and source credibility of rumor. Specifically, we identified three components of argument quality and four components of source credibility.

Keywords

Subject Classifications

62P25

References

[1]Sweetser, K.D. and E. Metzgar, Communicating during crisis: Use of blogs as a relationship management tool. Public Relations Review, 2007. 33(3): p. 340-342.
[2]Liu, F., A. Burton-Jones, and D. Xu. Rumors on social media in disasters: Extending transmission to retransmission. in Proceeding of the 19th Pacific Asia Conference on Information Systems. 2014.
[3]Kaur, A. and A. Sinha, Multi-contextual spammer detection for online social networks. Journal of Discrete Mathematical Sciences and Cryptography, 2021. 24(3): p. 777-786.
[4]Lenhart, A. and M. Madden, Teens, privacy & online social networks: How teens manage their online identities and personal information in the age of MySpace. 2007, Pew Internet & American Life Project.
[5]Ridings, C.M. and D. Gefen, Virtual community attraction: Why people hang out online. Journal of Computer‐Mediated Communication, 2004. 10(1): p. 1-15.
[6]TWNIC. Taiwan internet report. 2018 [cited 2021 7/19]; Available from: https://report.twnic.tw/2018/TWNIC_TaiwanInternetReport_2018_CH.pdf.
[7]TNMIC. A survey on broadband internet usage in Taiwan. 2015 [cited 2021 7/21]; Available from: http://www.twnic.net.tw/download/200307/20150901d.pdf.
[8]TWNIC. Broadband network usage survey in Taiwan. 2010 [cited 2021 12/28]; Available from: http://www.twnic.net.tw/download/200307/1001b.pdf.
[9]Ahmad, T., S.U. Ahmed, S.O. Ali, and R. Khan, Beginning with exploring the way for rumor free social networks. Journal of Statistics and Management Systems, 2020. 23(2): p. 231-238.
[10] Mittal, R. and M. Bhatia, Discovering bottlenecks entities in multi-layer social networks. Journal of Discrete Mathematical Sciences and Cryptography, 2019. 22(2): p. 241-252.
[11] Bakshy, E., I. Rosenn, C. Marlow, and L. Adamic, The role of social networks in information diffusion, in Proceedings of the 21st international conference on World Wide Web. 2012, ACM: Lyon, France. p. 519-528.
[12] Granovetter, M.S., The strength of weak ties. American Journal of Sociology, 1973: p. 1360-1380.
[13] Foster, E.K., Research on gossip: Taxonomy, methods, and future directions. Review of General Psychology, 2004. 8(2): p. 78-99.
[14] Rosnow, R.L. and E.K. Foster, Rumor and gossip research, in Psychological Science Agenda. 2005. p. 1-10.
[15] Miller, D.E., “Snakes in the greens” and rumor in the innercity. The Social Science Journal, 1992. 29(4): p. 381-393.
[16] Oh, O., M. Agrawal, and H.R. Rao, Community Intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly, 2013. 37(2): p. 407-426.
[17] Buckner, H.T., A theory of rumor transmission. Public Opinion Quarterly, 1965. 29(1): p. 54-70.
[18] Rosnow, R.L. and G.A. Fine, Rumor and gossip: The social psychology of hearsay. 1 ed. 1976, New York: Elsevier.
[19] Kapferer, J.-N., Rumors: Uses, interpretations, and images. 2013, New Brunswick: Routledge.
[20] McKnight, H. and C. Kacmar. Factors of information credibility for an internet advice site. in Proceedings of the 39th Annual Hawaii International Conference on System Sciences. 2006. Kauai, HI, USA: IEEE.
[21] Cacioppo, J.T. and R.E. Petty, Central and peripheral routes to persuasion: Application to advertising. 1 ed. Advertising and Consumer Psychology, ed. L.P.A. Woodside. 1983, Lexington, MA: D. C. . 3-23.
[22] Wathen, C.N. and J. Burkell, Believe it or not: Factors influencing credibility on the Web. Journal of the American Society for Information Science and Technology, 2002. 53(2): p. 134-144.
[23] Petty, R.E. and J.T. Cacioppo, Forewarning, cognitive responding, and resistance to persuasion. Journal of Personality and Social Psychology, 1977. 35(9): p. 645-655.
[24] Zhang, Y., Responses to humorous advertising: The moderating effect of need for cognition. Journal of Advertising, 1996. 25(1): p. 15-32.
[25] Cheung, M., C.-L. Sia, and K.K. Kuan, Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 2012. 13(8): p. 618-635.
[26] Sussman, S.W. and W.S. Siegal, Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 2003. 14(1): p. 47-65.
[27] Zhang, W. and S. Watts. Knowledge adoption in online communities of practice. in Proceedings of the International Conference on Information Systems. 2003. Seattle, Washington, USA: Association for Information Systems.
[28] Bhattacherjee, A. and C. Sanford, Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 2006. 30(4): p. 805-825.
[29] Luo, C., X.R. Luo, L. Schatzberg, and C.L. Sia, Impact of informational factors on online recommendation credibility: The moderating role of source credibility. Decision Support Systems, 2013. 56: p. 92-102.
[30] Soliha, E., B.S. Dharmmesta, B. Purwanto, and S.P. Syahlani, Message framing, source credibility, and consumer risk perception with motivation as moderating variable in functional food advertisements. American International Journal of Contemporary Research, 2014. 4(1): p. 193-208.
[31] Slater, M.D. and D. Rouner, How message evaluation and source attributes may influence credibility assessment and belief change. Journalism & Mass Communication Quarterly, 1996. 73(4): p. 974-991.
[32] Petty, R.E. and J.T. Cacioppo, Attitudes and persuasion: Classic and contemporary approaches. 1 ed. 1981, New York: Routledge 
[33] Petty, R.E. and K. Morris, Effects of need for cognition on message evaluation, recall, and persuasion. Journal of Personality and Social Psychology, 1983. 45(4): p. 805-818.
[34] Cacioppo, J.T., R.E. Petty, and K.J. Morris, Effects of need for cognition on message evaluation, recall, and persuasion. Journal of Personality and Social Psychology, 1983. 45(4): p. 805-818.
[35] Sia, C.-L., B.C. Tan, and K.-K. Wei, Can a GSS stimulate group polarization? An empirical study. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 1999. 29(2): p. 227-237.
[36] Bunker, A.M., Credibility and argument strength: persuasive effects when processing ability is impaired. 1 ed. 1994, East Lansing: Michigan State University Press.
[37] Nabi, R.L. and A. Hendriks, The persuasive effect of host and audience reaction shots in television talk shows. Journal of Communication, 2003. 53(3): p. 527-543.
[38] Fogg, B.J., Persuasive technology: using computers to change what we think and do, in Ubiquity. 2002. p. 1-5.
[39] Sternthal, B., L.W. Phillips, and R. Dholakia, The persuasive effect of scarce credibility: a situational analysis. Public Opinion Quarterly, 1978. 42(3): p. 285-314.
[40] Cheung, M.Y., C. Luo, C.L. Sia, and H. Chen, Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 2009. 13(4): p. 9-38.
[41] Hovland, C.I. and W. Weiss, The influence of source credibility on communication effectiveness. Public Opinion Quarterly, 1951. 15(4): p. 635-650.
[42] Lindgaard, G., Aesthetics, Visual Appeal, Usability and User Satisfaction: What Do the User’s Eyes Tell the User’s Brain? Australian Journal of Emerging Technologies & Society, 2007. 5(1): p. 1-14.
[43] Lindgaard, G., C. Dudek, D. Sen, L. Sumegi, and P. Noonan, An exploration of relations between visual appeal, trustworthiness and perceived usability of homepages. ACM Transactions on Computer-Human Interaction, 2011. 18(1): p. 1-30.
[44] Lavie, T. and N. Tractinsky, Assessing dimensions of perceived visual aesthetics of web sites. International Journal of Human-Computer Studies, 2004. 60(3): p. 269-298.
[45] Fogg, B.J., C. Soohoo, D.R. Danielson, L. Marable, J. Stanford, and E.R. Tauber. How do users evaluate the credibility of Web sites?: a study with over 2,500 participants. in Proceedings of the 2003 conference on Designing for user experiences. 2003. ACM.
[46] Karvonen, K. The beauty of simplicity. in Proceedings on the 2000 conference on Universal Usability. 2000. ACM.
[47] Fogg, B., E. Lee, and J. Marshall, Interactive technology and persuasion. The Handbook of Persuasion: Theory and Practice. Thousand Oaks, CA: Sage, 2002.
[48] Fogg, B. and H. Tseng. The elements of computer credibility. in Proceedings of the SIGCHI conference on Human Factors in Computing Systems. 1999. ACM.
[49] Koh, Y.J. and S.S. Sundar, Effects of specialization in computers, web sites, and web agents on e-commerce trust. International Journal of Human-Computer Studies, 2010. 68(12): p. 899-912.
[50] Nass, C., B. Reeves, and G. Leshner, Technology and roles: A tale of two TVs. Journal of Communication, 1996. 46(2): p. 121-128.
[51] Leshner, G., B. Reeves, and C. Nass, Switching channels: The effects of television channels on the mental representations of television news. Journal of Broadcasting & Electronic Media, 1998. 42(1): p. 21-33.
[52] Chaiken, S., Communicator physical attractiveness and persuasion. Journal of Personality and social Psychology, 1979. 37(8): p. 1387-1397.
[53] Joseph, W.B., The credibility of physically attractive communicators: A review. Journal of Advertising, 1982. 11(3): p. 15-24.
[54] Cheung, C.M.-Y., C.-L. Sia, and K.K. Kuan, Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 2012. 13(8): p. 618-635.
[55] Braddy, P.W., A.W. Meade, and C.M. Kroustalis, Online recruiting: The effects of organizational familiarity, website usability, and website attractiveness on viewers’ impressions of organizations. Computers in Human Behavior, 2008. 24(6): p. 2992-3001.
[56] Lee, C.S. and L. Ma, News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 2012. 28(2): p. 331-339.
[57] Fornell, C. and F. Bookstein, Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 1982. 19(4): p. 440-452.
[58] Pavlou, P.A. and O.A. El Sawy, From IT leveraging competence to competitive advantage in turbulent environments: The case of new product development. Information Systems Research, 2006. 17(3): p. 198-227.
[59] Diamantopoulos, A. and H.M. Winklhofer, Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 2001. 38(2): p. 269-277.
[60] Hair, J.F., J.J. Risher, M. Sarstedt, and C.M.J.E.B.R. Ringle, When to use and how to report the results of PLS-SEM. 2019. 31(1): p. 2-24.
[61] Hair Jr, J.F., G.T.M. Hult, C. Ringle, and M. Sarstedt, A primer on partial least squares structural equation modeling (PLS-SEM). 1 ed. 2016: Sage publications.
[62] Phang, C.W., J. Sutanto, A. Kankanhalli, Y. Li, B.C.Y. Tan, and H.H. Teo, Senior citizens’ acceptance of information systems: A study in the context of e-government services. IEEE Transactions on Engineering Management, 2006. 53(4): p. 555-569.
[63] Falk, R.F. and N.B. Miller, A primer for soft modeling. 1 ed. 1992: University of Akron Press.
Views: 294Downloads: 73Citations: 1