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.
|Title of host publication||18th International Conference on Advances in Mobile Computing and Multimedia, MoMM2020 - Proceedings|
|Editors||Pari Delir Haghighi, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Kotsis|
|Place of Publication||New York|
|Publisher||Association for Computing Machinery|
|Number of pages||9|
|Publication status||Published - 30 Nov 2020|
|Event||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 - Virtual, Online, Thailand|
Duration: 30 Nov 2020 → 2 Dec 2020
|Name||ACM International Conference Proceeding Series|
|Conference||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|
|Period||30/11/20 → 2/12/20|
Bibliographical noteFunding Information:
We acknowledge the AI-enabled Processes (AIP5) Research Centre and Data61 (CSIRO) for funding this research.
© 2020 ACM.
- cognitive information
- deep learning
- online social networks
- trust prediction