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

doi:10.1109/ACCESS.2020.3009445

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

School of Computing

Research output: Contribution to journal › Article › peer-review

60 Citations (Scopus)

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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 language	English
Pages (from-to)	144292-144309
Number of pages	18
Journal	IEEE Access
Volume	8
Early online date	16 Jul 2020
DOIs	https://doi.org/10.1109/ACCESS.2020.3009445
Publication status	Published - 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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10.1109/ACCESS.2020.3009445Licence: CC BY

Publisher version (open access)Final published version, 1.41 MBLicence: CC BY

Cite this

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title = "A survey on trust prediction in Online Social Networks",

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.",

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author = "Ghafari, \{Seyed Mohssen\} and Amin Beheshti and Aditya Joshi and Cecile Paris and Adnan Mahmood and Shahpar Yakhchi and Orgun, \{Mehmet A.\}",

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. ",

year = "2020",

doi = "10.1109/ACCESS.2020.3009445",

language = "English",

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T1 - A survey on trust prediction in Online Social Networks

AU - Ghafari, Seyed Mohssen

AU - Beheshti, Amin

AU - Joshi, Aditya

AU - Paris, Cecile

AU - Mahmood, Adnan

AU - Yakhchi, Shahpar

AU - Orgun, Mehmet A.

N1 - 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.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Context-aware

KW - Data sparsity problem

KW - Online Social Networks

KW - Pair-wise trust prediction

KW - Trust

KW - Trust relations

KW - Time-aware

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DO - 10.1109/ACCESS.2020.3009445

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VL - 8

SP - 144292

EP - 144309

JO - IEEE Access

JF - IEEE Access

ER -

A survey on trust prediction in Online Social Networks

Abstract

Bibliographical note

Keywords

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