An Analytical Review on Precursors of User'sTrust in Content Driven Health Websites


  • Sarika Gupta*, Himani Bansal


Trust, Trusted Health Websites, Accuracy, Sematic similarity, Privacy, CoronaVirus.


The availability of abundant information on health websites needs evaluation of websites' credibility to retain users' trust. We perform a systematic review to find the factors like Accuracy, Privacy, Semantic similarity, Transparency, etc., that impact users' trust in assessing the credibility of content-driven health websites. Published articles from 1997 to 2021 were examined. A THI(Trusted  Health Information ) model is proposed, emphasizing the factors with the hypothesis having a blazing role in the formulation of trust. An experiment is also conducted in a controlled environment where 30 users had to search the coronavirus information on five websites, including two government health websites (CDC and NIH ) and 3 Private owned health websites (WebMD, Mayoclinic, and Healthline). The questionnaire consisting of 24 questions based on each identified factor is prepared and answered by users. Based on users' responses, we rank the websites. We have used kappa analysis to check the reliability and consistency of the experiment. The degree of confidence is higher than the degree of confidence achieved by chance on all the websites. We compare our model results with the WOT(web of trust) and find a positive correlation with a value of 0.73 that verifies our model.



How to Cite

Sarika Gupta*, Himani Bansal. (2022). An Analytical Review on Precursors of User’sTrust in Content Driven Health Websites. Computer Integrated Manufacturing Systems, 28(10), 480–542. Retrieved from