A survey on trust prediction in Online Social Networks

Seyed Mohssen Ghafari, Amin Beheshti, Aditya Joshi, Cecile Paris, Adnan Mahmood, Shahpar Yakhchi, Mehmet A. Orgun

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Abstract

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.
Original languageEnglish
Pages (from-to)144292-144309
Number of pages18
JournalIEEE Access
Volume8
Early online date16 Jul 2020
DOIs
Publication statusPublished - 2020

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Context-aware
  • Data sparsity problem
  • Online Social Networks
  • Pair-wise trust prediction
  • Trust
  • Trust relations
  • Time-aware

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