Reputation and social network analysis in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
Operators for propagating trust and their evaluation in social networks
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning causality for news events prediction
Proceedings of the 21st international conference on World Wide Web
Using proximity to predict activity in social networks
Proceedings of the 21st international conference companion on World Wide Web
SARC: subjectivity alignment for reputation computation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A Generalized Stereotypical Trust Model
TRUSTCOM '12 Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
Trust modeling for opinion evaluation by coping with subjectivity and dishonesty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In open online communities, everyone can freely express opinions about other entities. As the quality of opinions may vary, it is important for users to evaluate opinions in order to determine how much to rely on. In this paper, we propose a novel trust model stemmed from the diffusion theory in social science (called DiffTrust), to evaluate the opinions of users (referred to as advisors) by modeling their trustworthiness. Specifically, an advisor's trust building among users is considered as a diffusion process. Her trustworthiness perceived by a specific user is influenced by four important factors including the advisor's characteristics directly observed by the user, susceptibility of the user, the contagious influence of other users already having a certain level of trust on the advisor and the environment. DiffTrust also emphasizes on the dynamics of trust. Experimental results based on four real datasets verify the effectiveness of our model in comparison with state-of-the-art approaches.