SETTRUST: Social Exchange Theory based context-aware trust prediction in Online Social Networks

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

Trust is context-dependent. In real-world scenarios, people trust each other only in certain contexts. However, this concept has not been seriously taken into account in most of the existing trust prediction approaches in Online Social Networks (OSNs). In addition, very few attempts have been made on trust prediction based on social psychology theories. For decades, social psychology theories have attempted to explain people’s behaviors in social networks; hence, employing such theories for trust prediction in OSNs will enhance accuracy. In this paper, we apply a well-known psychology theory, called Social Exchange Theory (SET), to evaluate the potential trust relation between users in OSNs. Based on SET, one person starts a relationship with another person, if and only if the costs of that relationship are less than its benefits. To evaluate potential trust relations in OSNs based on SET, we first propose some factors to capture the costs and benefits of a relationship. Then, based on these factors, we propose a trust metric called Trust Degree; at that point, we propose a trust prediction method, based on Matrix Factorization and apply the context of trust in a mathematical model. Finally, we conduct experiments on two real-world datasets to demonstrate the superior performance of our approach over the state-of-the-art approaches.

LanguageEnglish
Title of host publicationData Quality and Trust in Big Data
Subtitle of host publication5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers
EditorsHakim Hacid, Quan Z. Sheng, Tetsuya Yoshida, Azadeh Sarkheyli, Rui Zhou
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Pages46-61
Number of pages16
ISBN (Electronic)9783030191436
ISBN (Print)9783030191429
DOIs
Publication statusPublished - 1 Jan 2019
Event5th International Workshop on Data Quality and Trust in Big Data, QUAT 2018, held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018 - Dubai, United Arab Emirates
Duration: 12 Nov 201815 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11235 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Data Quality and Trust in Big Data, QUAT 2018, held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018
CountryUnited Arab Emirates
CityDubai
Period12/11/1815/11/18

Fingerprint

Context-aware
Social Networks
Prediction
Factorization
Costs
Mathematical models
Person
Matrix Factorization
Evaluate
Experiments
Mathematical Model
If and only if
Metric
Scenarios
Dependent

Keywords

  • Fake news
  • Social Exchange Theory
  • Social networks analytics
  • Trust prediction

Cite this

Ghafari, S. M., Yakhchi, S., Beheshti, A., & Orgun, M. (2019). SETTRUST: Social Exchange Theory based context-aware trust prediction in Online Social Networks. In H. Hacid, Q. Z. Sheng, T. Yoshida, A. Sarkheyli, & R. Zhou (Eds.), Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers (pp. 46-61). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11235 LNCS). Springer-VDI-Verlag GmbH & Co. KG. https://doi.org/10.1007/978-3-030-19143-6_4
Ghafari, Seyed Mohssen ; Yakhchi, Shahpar ; Beheshti, Amin ; Orgun, Mehmet. / SETTRUST : Social Exchange Theory based context-aware trust prediction in Online Social Networks. Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. editor / Hakim Hacid ; Quan Z. Sheng ; Tetsuya Yoshida ; Azadeh Sarkheyli ; Rui Zhou. Springer-VDI-Verlag GmbH & Co. KG, 2019. pp. 46-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{c65267063f004310ba0f4ef47fa38eb4,
title = "SETTRUST: Social Exchange Theory based context-aware trust prediction in Online Social Networks",
abstract = "Trust is context-dependent. In real-world scenarios, people trust each other only in certain contexts. However, this concept has not been seriously taken into account in most of the existing trust prediction approaches in Online Social Networks (OSNs). In addition, very few attempts have been made on trust prediction based on social psychology theories. For decades, social psychology theories have attempted to explain people’s behaviors in social networks; hence, employing such theories for trust prediction in OSNs will enhance accuracy. In this paper, we apply a well-known psychology theory, called Social Exchange Theory (SET), to evaluate the potential trust relation between users in OSNs. Based on SET, one person starts a relationship with another person, if and only if the costs of that relationship are less than its benefits. To evaluate potential trust relations in OSNs based on SET, we first propose some factors to capture the costs and benefits of a relationship. Then, based on these factors, we propose a trust metric called Trust Degree; at that point, we propose a trust prediction method, based on Matrix Factorization and apply the context of trust in a mathematical model. Finally, we conduct experiments on two real-world datasets to demonstrate the superior performance of our approach over the state-of-the-art approaches.",
keywords = "Fake news, Social Exchange Theory, Social networks analytics, Trust prediction",
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Ghafari, SM, Yakhchi, S, Beheshti, A & Orgun, M 2019, SETTRUST: Social Exchange Theory based context-aware trust prediction in Online Social Networks. in H Hacid, QZ Sheng, T Yoshida, A Sarkheyli & R Zhou (eds), Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11235 LNCS, Springer-VDI-Verlag GmbH & Co. KG, pp. 46-61, 5th International Workshop on Data Quality and Trust in Big Data, QUAT 2018, held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, 12/11/18. https://doi.org/10.1007/978-3-030-19143-6_4

SETTRUST : Social Exchange Theory based context-aware trust prediction in Online Social Networks. / Ghafari, Seyed Mohssen; Yakhchi, Shahpar; Beheshti, Amin; Orgun, Mehmet.

Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. ed. / Hakim Hacid; Quan Z. Sheng; Tetsuya Yoshida; Azadeh Sarkheyli; Rui Zhou. Springer-VDI-Verlag GmbH & Co. KG, 2019. p. 46-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11235 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

TY - GEN

T1 - SETTRUST

T2 - Social Exchange Theory based context-aware trust prediction in Online Social Networks

AU - Ghafari, Seyed Mohssen

AU - Yakhchi, Shahpar

AU - Beheshti, Amin

AU - Orgun, Mehmet

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Trust is context-dependent. In real-world scenarios, people trust each other only in certain contexts. However, this concept has not been seriously taken into account in most of the existing trust prediction approaches in Online Social Networks (OSNs). In addition, very few attempts have been made on trust prediction based on social psychology theories. For decades, social psychology theories have attempted to explain people’s behaviors in social networks; hence, employing such theories for trust prediction in OSNs will enhance accuracy. In this paper, we apply a well-known psychology theory, called Social Exchange Theory (SET), to evaluate the potential trust relation between users in OSNs. Based on SET, one person starts a relationship with another person, if and only if the costs of that relationship are less than its benefits. To evaluate potential trust relations in OSNs based on SET, we first propose some factors to capture the costs and benefits of a relationship. Then, based on these factors, we propose a trust metric called Trust Degree; at that point, we propose a trust prediction method, based on Matrix Factorization and apply the context of trust in a mathematical model. Finally, we conduct experiments on two real-world datasets to demonstrate the superior performance of our approach over the state-of-the-art approaches.

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KW - Fake news

KW - Social Exchange Theory

KW - Social networks analytics

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M3 - Conference proceeding contribution

SN - 9783030191429

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 46

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PB - Springer-VDI-Verlag GmbH & Co. KG

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Ghafari SM, Yakhchi S, Beheshti A, Orgun M. SETTRUST: Social Exchange Theory based context-aware trust prediction in Online Social Networks. In Hacid H, Sheng QZ, Yoshida T, Sarkheyli A, Zhou R, editors, Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. Springer-VDI-Verlag GmbH & Co. KG. 2019. p. 46-61. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-19143-6_4