AN INNOVATIVE APPROACH TO DERIVE TRUST FROM SOCIAL NETWORKS AND TO IMPROVE THE MATCHING IN DENTAL CARE RECOMMENDATION SYSTEMS
Social trust has been gradually transitioned from face to face to online platforms due to the increasing engagement by the internet users in online social networks. This study looks at how this affects the way medical professionals, and dentists in particular, are recommended and chosen. Based on the literature, analysis of online dental reviews and a survey, it finds that subjective qualities of both dentists and patients are important aspects of the social trust. In order to analyse those qualities, this study introduces an innovative trust-based information model to evaluate those important subjective qualities. The model evaluates 4 trust components: context, relationship, reputation and personality analysis. Dentists and patients are profiled using this model and information extracted from social networks. Dentists are profiled using subjective qualities derived from online dental reviews and patients are profiled using subjective information such as level of dental fear and personality traits, collected from the survey with 580 participants. This paper provides an overview of dentists’ profiles from online reviews and that of patients from the survey results, on a particular example as an illustration. The result of this study can be used to define a set of rules to improve the matching between patients and dentists in dental care recommendation systems.
Pradhan, Sojen; Gay, Valerie; and Nepal, Surya, "AN INNOVATIVE APPROACH TO DERIVE TRUST FROM SOCIAL NETWORKS AND TO IMPROVE THE MATCHING IN DENTAL CARE RECOMMENDATION SYSTEMS" (2016). PACIS 2016 Proceedings. 328.