Using Bayesian networks with hidden variables for identifying trustworthy users in social networks

Abstract

The popularity and broad accessibility of online social networks (OSNs) have facilitated effective communication among people, but such networks also pose potential risks that should not be ignored. Interaction through OSNs is complex and can be unsafe, as individuals can be contacted by strangers at any time. This makes the notion of trust a crucial issue in the use of OSNs. However, compared with decision-making processes associated with whether to trust a stranger encountered in everyday life, this task is more difficult to address with regard to OSNs due to the lack of face-to-face communication and prior knowledge between people. In this article, trust evaluation is formalised as a classification problem. We demonstrate how user profiles and historical records can be organised into a logical structure based on Bayesian networks to recognise the trustworthy people without the need to build trust relationships in OSNs. This is possible when a more detailed description of features denoted by hidden variables is considered. We compare the performance of our method with those of six other machine learning methods using Facebook and Twitter datasets, and our results show that our method achieves higher values in accuracy, recall and F1 score.