TY - GEN
T1 - A dynamic deep trust prediction approach for online social networks
AU - Ghafari, Seyed Mohssen
AU - Beheshti, Amin
AU - Joshi, Aditya
AU - Paris, Cecile
AU - Yakhchi, Shahpar
AU - Jolfaei, Alireza
AU - Orgun, Mehmet A.
PY - 2020
Y1 - 2020
N2 - Trust can be employed for finding reliable information in Online Social Networks (OSNs). Since users in OSNs may intentionally change their behavior over time (in some cases for deceiving other users), modeling (pair-wise) trust relations in such complex environment is a challenging task. However, most of the existing trust prediction approaches assume that trust relations are fixed over time and they fail to capture the dynamic behavior of users in OSNs. In this paper, we propose a dynamic deep trust prediction model. As the impact of incidental emotions on trust has been proven in psychology studies, in this paper, we also study this impact on our trust prediction approach. First, we propose a novel deep structure that incorporates users' emotions and their textual contents in OSNs. Second, we use embeddings to represent the users and their self-descriptions provided. Finally, considering different time windows, we dynamically predict pair-wise trust relations. To evaluate our approach, we collected a large twitter dataset. The evaluation results demonstrate the effectiveness of our approach compared to the state-of-the-art approaches.
AB - Trust can be employed for finding reliable information in Online Social Networks (OSNs). Since users in OSNs may intentionally change their behavior over time (in some cases for deceiving other users), modeling (pair-wise) trust relations in such complex environment is a challenging task. However, most of the existing trust prediction approaches assume that trust relations are fixed over time and they fail to capture the dynamic behavior of users in OSNs. In this paper, we propose a dynamic deep trust prediction model. As the impact of incidental emotions on trust has been proven in psychology studies, in this paper, we also study this impact on our trust prediction approach. First, we propose a novel deep structure that incorporates users' emotions and their textual contents in OSNs. Second, we use embeddings to represent the users and their self-descriptions provided. Finally, considering different time windows, we dynamically predict pair-wise trust relations. To evaluate our approach, we collected a large twitter dataset. The evaluation results demonstrate the effectiveness of our approach compared to the state-of-the-art approaches.
KW - online social networks
KW - deep learning
KW - trust prediction
KW - cognitive information
UR - http://www.scopus.com/inward/record.url?scp=85100456628&partnerID=8YFLogxK
U2 - 10.1145/3428690.3429167
DO - 10.1145/3428690.3429167
M3 - Conference proceeding contribution
AN - SCOPUS:85100456628
T3 - ACM International Conference Proceeding Series
SP - 11
EP - 19
BT - MoMM2020 - 18th International Conference on Advances in Mobile Computing and Multimedia, MoMM2020 - Proceedings
A2 - Haghighi, Pari Delir
A2 - Salvadori, Ivan Luiz
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
A2 - Kotsis, Gabriele
PB - Association for Computing Machinery (ACM)
CY - New York, NY
T2 - 18th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2020, in conjunction with the 22nd International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2020
Y2 - 30 November 2020 through 2 December 2020
ER -