A Survey on Trust Prediction in Online Social Networks

• Published in: IEEE access Vol. 8; pp. 144292 - 144309
• Main Authors: Ghafari, Seyed Mohssen, Beheshti, Amin, Joshi, Aditya, Paris, Cecile, Mahmood, Adnan, Yakhchi, Shahpar, Orgun, Mehmet A.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Australia; Business; Computer science; Context-aware; data sparsity problem; online social networks; pair-wise trust prediction; Prediction models; Predictions; Psychology; Recommender systems; Social network services; Social networks; Sociology; time-aware; trust; trust relations; Trustworthiness
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3009445

Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.