Trust assessment in social networks

Social network interactions and web/IoT-based applications have led to data getting generated at a very rapid rate. Amongst these, social networking sites continue to have a comparatively greater influence on the day-to-day life of an individual. Social networking sites are the main vehicles for disseminating information due to their inherent global outreach. Due to popularity of social network sites, every smart phone user, government, corporate and political parties are able to keep up to date with the latest information. However, this information may also contain some misinformation and/or disinformation, which could manipulate and influence the opinion of an individual or a group of individuals. In order to minimize misinformation and disinformation in such networks, a veracity assessment in the form of trust, needs to be computed for each and every data included in it, in order to ensure secure and trustworthy data availability for the users. Many researchers have developed models for assessing veracity in the form of trust for different social networking sites. This paper focuses on a detailed review of trust-based models in social networks. The paper also delineates how machine learning models have been used for trust assessment in social networks.