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A neural computing approach to the construction of information credibility assessments for online social networks

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
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Abstract

Information credibility assessment has traditionally been understood in the online information processing context. This study extends this literature by exploring information credibility assessment mechanisms in the online social network context. In doing so, it responds to the emerging call for an understanding of the online social network context under the operational mechanism of information credibility assessment. Specifically, this study proposes a neural computing approach based on the stimulate–organism–response framework to determine the process, strategy and clue of online social network information assessment. Drawing on the theory of the Technology Acceptance Model and the theory of reasoned action, and based on a survey of 399 returned online questionnaires, we find that the involvement of online social network information, information sources and information transfer channels has a significant positive correlation with information usefulness and credibility, while information scenarios have no significant impact on usefulness and credibility, and the credibility of information is significant to the user’s information adoption behavior. Different from the predictions made in the literature, the information scenario does not affect information credibility and information usefulness. Conclusions and academic and practical implications are discussed.

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References

  1. Guille A et al (2013) Information diffusion in online social networks: a survey. ACM SIGMOD Rec 42(1):17–28

    Article  Google Scholar 

  2. Shi J, Lai KK, Hu P, Chen G (2017) Understanding and predicting individual retweeting behavior: receiver perspectives. Appl Soft Comput 60:844–857

    Article  Google Scholar 

  3. Wang D, Chen Y, Chen D (2018) Efficiency optimization and simulation to manufacturing and service systems based on manufacturing technology Just-In-Time. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-018-1161-2

    Article  Google Scholar 

  4. Wang L, Qian D, Zhu L (2018) The effect of system granted cues on microblog rewarding repost behavior—a source credibility respective. J Electron Commer Res 19(1):104–118

    Google Scholar 

  5. Wang D, Zha Y, Bi G et al (2018) A meta-analysis of satisfaction–loyalty relationship in E-commerce: sample and measurement characteristics as moderators. Wirel Pers Commun. https://doi.org/10.1007/s11277-018-5488-9

    Article  Google Scholar 

  6. Liu H, Chen Y, Zha Y et al (2018) The effect of satisfaction on loyalty in consumption and service industry based on meta-analysis and it’s algorithm. Wirel Pers Commun. https://doi.org/10.1007/s11277-018-5489-8

    Article  Google Scholar 

  7. Horning MA (2017) Interacting with news: exploring the effects of modality and perceived responsiveness and control on news source credibility and enjoyment among second screen viewers. Comput Hum Behav 73:273–283

    Article  Google Scholar 

  8. Metzger MJ, Flanagin AJ, Eyal K, Lemus DR, McCann RM (2003) Credibility for the 21st century: integrating perspectives on source, message, and media credibility in the contemporary media environment. Commun Yearb 27:293–336

    Article  Google Scholar 

  9. Cox D, Cox JG, Cox AD (2017) To Err is human? How typographical and orthographical errors affect perceptions of online reviewers. Comput Hum Behav 75:245–253

    Article  Google Scholar 

  10. Durcikova A, Lee AS, Brown SA (2018) Making rigorous research relevant: innovating statistical action research. MIS Q 42(1):241–263

    Article  Google Scholar 

  11. Khan IU, Yu Y, Hameed Z, Khan SU, Waheed A (2018) Assessing the physicians’ acceptance of E-prescribing in a developing country: an extension of the UTAUT model with moderating effect of perceived organizational support. J Glob Inf Manag (JGIM) 26(3):121–142

    Article  Google Scholar 

  12. Carillo K, Scornavacca E, Za S (2017) The role of media dependency in predicting continuance intention to use ubiquitous media systems. Inf Manag 54(3):317–335

    Article  Google Scholar 

  13. Burkley E, Anderson D, Curtis J (2011) You wore me down: self-control strength and social influence. Soc Personal Psychol Compass 5(7):487–499

    Article  Google Scholar 

  14. Wang D, Chen Y, Xu J (2017) Knowledge management of web financial reporting in human–computer interactive perspective. EURASIA J Math Sci Technol Edu 13:1–25

    Google Scholar 

  15. Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s Mechanical Turk: a new source of inexpensive, yet high-quality, data? Perspect Psychol Sci 6(1):3–5

    Article  Google Scholar 

  16. Yamamoto M, Nah S (2018) A multilevel examination of local newspaper credibility. Journal Mass Commun Q 1(95):76–95

    Article  Google Scholar 

  17. Greer J, Pan PL (2015) The role of website format, blog use, and information-gathering acquaintance in online message assessment. Telemat Inform 32(4):594–602

    Article  Google Scholar 

  18. McLeod DM, Wise D, Perryman M (2017) Thinking about the media: a review of theory and research on media perceptions, media effects perceptions, and their consequences. Rev Commun Res 5:35–83

    Google Scholar 

  19. Balouchi M, Aziz YA, Hasangholipour T, Khanlari A, Abd Rahman A, Raja-Yusof RN (2017) Explaining and predicting online tourists’ behavioural intention in accepting consumer generated contents. J Hosp Tour Technol 8(2):168–189

    Google Scholar 

  20. VanderMolen A, Cacciatore MA, Meng J, Reber BH (2015) Media-Source preferences and trust building: how they influence relationship management. Int J Strateg Commun 9(1):1–22

    Article  Google Scholar 

  21. Edwards C, Edwards A, Spence PR, Shelton AK (2014) Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Comput Hum Behav 33:372–376

    Article  Google Scholar 

  22. von Sikorski C, Knoll J (2018) All at once or bit by bit? How the serialization of news affects recipients’ attitudes toward politicians involved in scandals. Int J Commun 12:19

    Google Scholar 

  23. Martins N, Weaver AJ, Lynch T (2018) What the public “knows” about media effects research: the influence of news frames on perceived credibility and belief change. J Commun 1(68):98–119. https://doi.org/10.1093/joc/jqx004

    Article  Google Scholar 

  24. Zaichkowsky JL (1986) Conceptualizing involvement. J Advert 15(2):4–34

    Article  Google Scholar 

  25. Olien CN, Tichenor PJ, Donohue GA (2018) A guard dog perspective on the role of media. J Commun 4(2):115–132

    Google Scholar 

  26. Comber R, Thieme A (2013) Designing beyond habit: opening space for improved recycling and food waste behaviors through processes of persuasion, social influence and aversive affect. Pers Ubiquit Comput 6(17):1197–1210. https://doi.org/10.1007/s00779-012-0587-1

    Article  Google Scholar 

  27. Luca M, Zervas G (2016) Fake it till you make it: reputation, competition, and yelp review fraud. Manag Sci. https://doi.org/10.1287/mnsc.2015.2304

    Article  Google Scholar 

  28. Chen Y, Wang D, Bi G (2018) An image edge recognition approach based on multi-operator dynamic weight detection in virtual reality scenario. Cluster Comput. https://doi.org/10.1007/s10586-017-1604-y

  29. Johnson TJ, Kaye BK (2014) Credibility of social network sites for political information among politically interested internet users. J Comput Mediat Commun 19(4):957–974

    Article  Google Scholar 

  30. Unkel J, Haas A (2017) The effects of credibility cues on the selection of search engine results. J Assoc Inf Sci Technol. https://doi.org/10.1002/asi.23820

    Article  Google Scholar 

  31. Jensen ML, Yetgin E (2017) Prominence and interpretation of online conflict of interest disclosures. MIS Q 41(2):629–643

    Article  Google Scholar 

  32. Petty RE, Cacioppo JT (1986) The elaboration likelihood model of persuasion. Springer, New York. https://doi.org/10.1007/978-1-4612-4964-1_1

    Book  Google Scholar 

  33. Huang Y, Shen F (2016) Effects of cultural tailoring on persuasion in cancer communication: a meta-analysis. J Commun 4(66):694–715. https://doi.org/10.1111/jcom.12243

    Article  Google Scholar 

  34. Ruokolainen J, Aarikka-Stenroos L (2016) Rhetoric.in customer referencing: fortifying sales arguments in two start-up companies. Ind Mark Manag 54:188–202. https://doi.org/10.1016/j.indmarman.2015.08.002

    Article  Google Scholar 

  35. Zhang H, Zhang X, Zhou S (2017) To trust or not to trust: characteristic-based and process-based trust. China Media Res 13(1):29–41

    MathSciNet  Google Scholar 

  36. Mansour A, Francke H (2017) Credibility assessments of everyday life information on Facebook: a sociocultural investigation of a group of mothers. Inf Res 22(2), paper 750. Retrieved from http://InformationR.net/ir/22-2/paper750.html

  37. Evans JSBT, Stanovich KE (2013) Dual-process theories of higher cognition: advancing the debate. Perspect Psychol Sci 3(8):223–241. https://doi.org/10.1177/1745691612460685

    Article  Google Scholar 

  38. Lin X, Spence PR, Lachlan KA (2016) Social media and credibility indicators: the effect of influence cues. Comput Hum Behav 63:264–271

    Article  Google Scholar 

  39. Jin Z, Cao J, Zhang Y, Zhou J, Tian Q (2017) Novel visual and statistical image features for microblogs news verification. IEEE Trans Multimed 19(3):598–608

    Article  Google Scholar 

  40. Gao Q, Tian Y, Tu M (2015) Exploring factors influencing Chinese user’s perceived credibility of health and safety information on Weibo. Comput Hum Behav 45:21–31

    Article  Google Scholar 

  41. Sundar SS (2008) The MAIN model: a heuristic approach to understanding technology effects on credibility. In: Metzger MJ, Flanagin AJ (eds) Digital media and learning. The MIT Press, Cambridge, pp. 73–100

    Google Scholar 

  42. Viviani M, Pasi G (2017) Quantifier guided aggregation for the veracity assessment of online reviews. Int J Intell Syst 32(5):481–501

    Article  Google Scholar 

  43. Liu YL, Keeling KA, Papamichail KN (2016) Maximising the credibility of realistic job preview messages: the effect of jobseekers’ decision-making style on recruitment information credibility. Int J Hum Resour Manag 29(7):1–35

    Google Scholar 

  44. Roper Organization (1979) Public perception of television and other mass media: Public attitudes 1959-1978. New York: Television Information Office

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Acknowledgements

This paper is supported by grants from the National Natural Science Foundation of China (71801056, 71801059, 71731010), the Humanities and Social Sciences of the Ministry of Education of China (18YJC630017), the Natural Science Foundation of Guangdong Province (2015A030310506), the Philosophy and Social Science Planning Program of Guangdong Province (GD16XGL38), the Philosophy and Social Science Planning Program of Guangzhou (2016GZQN32), and the Yangchengxueren Program of Philosophy and Social Science Planning Program of Guangzhou (18QNXR34).

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Authors and Affiliations

  1. School of Management, Guangzhou University, Guangzhou, 510006, People’s Republic of China

    Dong Wang

  2. School of Insurance, Guangdong University of Finance, Guangzhou, 510521, People’s Republic of China

    Yujing Chen

  3. School of Management, Guangdong University of Technology, Guangzhou, 510520, People’s Republic of China

    Yujing Chen

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  1. Dong Wang
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  2. Yujing Chen
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Correspondence to Yujing Chen.

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Wang, D., Chen, Y. A neural computing approach to the construction of information credibility assessments for online social networks. Neural Comput & Applic 31 (Suppl 1), 259–275 (2019). https://doi.org/10.1007/s00521-018-3734-4

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  • DOI: https://doi.org/10.1007/s00521-018-3734-4

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