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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
  • 95 Downloads

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.

Keywords

Online social network Information credibility assessment SOR 

Notes

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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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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