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.
| Language | English |
|---|---|
| Title of host publication | Data Quality and Trust in Big Data |
| Subtitle of host publication | 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers |
| Editors | Hakim Hacid, Quan Z. Sheng, Tetsuya Yoshida, Azadeh Sarkheyli, Rui Zhou |
| Publisher | Springer-VDI-Verlag GmbH & Co. KG |
| Pages | 46-61 |
| Number of pages | 16 |
| ISBN (Electronic) | 9783030191436 |
| ISBN (Print) | 9783030191429 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
| Event | 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 Duration: 12 Nov 2018 → 15 Nov 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11235 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 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 |
|---|---|
| Country | United Arab Emirates |
| City | Dubai |
| Period | 12/11/18 → 15/11/18 |
Fingerprint
Keywords
- Fake news
- Social Exchange Theory
- Social networks analytics
- Trust prediction
Cite this
}
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 proceeding › Conference proceeding contribution › Research › peer-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.
AB - 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.
KW - Fake news
KW - Social Exchange Theory
KW - Social networks analytics
KW - Trust prediction
UR - http://www.scopus.com/inward/record.url?scp=85065482008&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-19143-6_4
DO - 10.1007/978-3-030-19143-6_4
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
EP - 61
BT - Data Quality and Trust in Big Data
A2 - Hacid, Hakim
A2 - Sheng, Quan Z.
A2 - Yoshida, Tetsuya
A2 - Sarkheyli, Azadeh
A2 - Zhou, Rui
PB - Springer-VDI-Verlag GmbH & Co. KG
ER -