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