A dynamic deep trust prediction approach for online social networks

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

Abstract

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.

Original languageEnglish
Title of host publicationMoMM2020 - 18th International Conference on Advances in Mobile Computing and Multimedia, MoMM2020 - Proceedings
EditorsPari Delir Haghighi, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Kotsis
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages11-19
Number of pages9
ISBN (Electronic)9781450389242
DOIs
Publication statusPublished - 2020
Event18th 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 20202 Dec 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference18th 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
CountryThailand
CityVirtual, Online
Period30/11/202/12/20

Keywords

  • online social networks
  • deep learning
  • trust prediction
  • cognitive information

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