Neural Computing and Applications

, Volume 31, Supplement 1, pp 259–275 | Cite as

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

  • Dong Wang
  • Yujing ChenEmail author
S.I. : Machine Learning Applications for Self-Organized Wireless Networks


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.


Online social network Information credibility assessment SOR 



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).


  1. 1.
    Guille A et al (2013) Information diffusion in online social networks: a survey. ACM SIGMOD Rec 42(1):17–28Google Scholar
  2. 2.
    Shi J, Lai KK, Hu P, Chen G (2017) Understanding and predicting individual retweeting behavior: receiver perspectives. Appl Soft Comput 60:844–857Google Scholar
  3. 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. Google Scholar
  4. 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):104–118Google Scholar
  5. 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. Google Scholar
  6. 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. Google Scholar
  7. 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 73:273–283Google Scholar
  8. 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 27:293–336Google Scholar
  9. 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 75:245–253Google Scholar
  10. 10.
    Durcikova A, Lee AS, Brown SA (2018) Making rigorous research relevant: innovating statistical action research. MIS Q 42(1):241–263Google Scholar
  11. 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):121–142Google Scholar
  12. 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):317–335Google Scholar
  13. 13.
    Burkley E, Anderson D, Curtis J (2011) You wore me down: self-control strength and social influence. Soc Personal Psychol Compass 5(7):487–499Google Scholar
  14. 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 13:1–25Google Scholar
  15. 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):3–5Google Scholar
  16. 16.
    Yamamoto M, Nah S (2018) A multilevel examination of local newspaper credibility. Journal Mass Commun Q 1(95):76–95Google Scholar
  17. 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):594–602Google Scholar
  18. 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 5:35–83Google Scholar
  19. 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):168–189Google Scholar
  20. 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):1–22Google Scholar
  21. 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 33:372–376Google Scholar
  22. 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 12:19Google Scholar
  23. 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. Google Scholar
  24. 24.
    Zaichkowsky JL (1986) Conceptualizing involvement. J Advert 15(2):4–34Google Scholar
  25. 25.
    Olien CN, Tichenor PJ, Donohue GA (2018) A guard dog perspective on the role of media. J Commun 4(2):115–132Google Scholar
  26. 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. Google Scholar
  27. 27.
    Luca M, Zervas G (2016) Fake it till you make it: reputation, competition, and yelp review fraud. Manag Sci. Google Scholar
  28. 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.
  29. 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):957–974Google Scholar
  30. 30.
    Unkel J, Haas A (2017) The effects of credibility cues on the selection of search engine results. J Assoc Inf Sci Technol. Google Scholar
  31. 31.
    Jensen ML, Yetgin E (2017) Prominence and interpretation of online conflict of interest disclosures. MIS Q 41(2):629–643Google Scholar
  32. 32.
    Petty RE, Cacioppo JT (1986) The elaboration likelihood model of persuasion. Springer, New York. Google Scholar
  33. 33.
    Huang Y, Shen F (2016) Effects of cultural tailoring on persuasion in cancer communication: a meta-analysis. J Commun 4(66):694–715. Google Scholar
  34. 34.
    Ruokolainen J, Aarikka-Stenroos L (2016) customer referencing: fortifying sales arguments in two start-up companies. Ind Mark Manag 54:188–202. Google Scholar
  35. 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):29–41MathSciNetGoogle Scholar
  36. 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
  37. 37.
    Evans JSBT, Stanovich KE (2013) Dual-process theories of higher cognition: advancing the debate. Perspect Psychol Sci 3(8):223–241. Google Scholar
  38. 38.
    Lin X, Spence PR, Lachlan KA (2016) Social media and credibility indicators: the effect of influence cues. Comput Hum Behav 63:264–271Google Scholar
  39. 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):598–608Google Scholar
  40. 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 45:21–31Google Scholar
  41. 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. 73–100Google Scholar
  42. 42.
    Viviani M, Pasi G (2017) Quantifier guided aggregation for the veracity assessment of online reviews. Int J Intell Syst 32(5):481–501Google Scholar
  43. 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):1–35Google Scholar
  44. 44.
    Roper Organization (1979) Public perception of television and other mass media: Public attitudes 1959-1978. New York: Television Information OfficeGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of ManagementGuangzhou UniversityGuangzhouPeople’s Republic of China
  2. 2.School of InsuranceGuangdong University of FinanceGuangzhouPeople’s Republic of China
  3. 3.School of ManagementGuangdong University of TechnologyGuangzhouPeople’s Republic of China

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