How Much I Can Rely on You: Measuring Trustworthiness of a Twitter User

Trustworthiness in an online environment is essential because individuals and organizations can easily be misled by false and malicious information receiving from untrustworthy users. Though existing methods assess users' trustworthiness by exploiting Twitter account properties, their efficacy is inadequate because of Twitter's restriction on profile and tweet size, the existence of missing or insufficient profiles, and ease to create fake accounts or relationships to pretend as trustworthy. In this paper, we present a holistic approach by exploiting ideas perceived from real-world organizations for trust estimation along with available Twitter information. Users' trustworthiness is determined by considering their credentials, recommendation from referees and the quality of the information in their Twitter accounts and tweets. We establish the feasibility of our approach analytically and further devise a multi-objective cost function for the A* search to find a quasi-optimal path between the trust evaluator and the user whose trustworthiness is being evaluated. We also propose an incentive mechanism to increase user participation in the trust evaluation process, and a threat model and trustworthiness measure of referees to thwart the possibility of providing an untruthful recommendation to inflate one's trustworthiness. The efficacy of our proposed approach is validated through experiments using Twitter data and extensive simulation in various scenarios.