Paper Info
Title
Predicting the Evolution of Hot Topics: A Solution Based on the Online Opinion Dynamics Model in Social Network
Abstract
Predicting and utilizing the evolution trend of hot topics is critical for contingency management and decision-making purposes of government bodies and enterprises. This paper proposes a model named online opinion dynamics (OODs) where any node in a social network has its unique confidence threshold and influence radius. The nodes in the OOD are mainly affected by their neighbors and are also randomly influenced by unfamiliar nodes. In the traditional opinion model, however, each node is affected by all other nodes, including its friends. Furthermore, many traditional opinion evolution approaches are reviewed to see if all nodes (participants) can eventually reach a consensus. On the contrary, OOD is more focused on such details as concluding the overall trend of events and evaluating the support level of each participant through numerical simulation. Experiments show that OOD is superior to the improvement of the original Hegselmann-Krause (HK) model, HK-13 and HK-17, with respect to qualitative predictions of the evolution trend of an event. The quantitative predictions of the HK model cannot be used to make decisions, whereas the results of the OOD model are proved to be acceptable.
Year
DOI
Venue
2020
10.1109/TSMC.2018.2876235
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Decision-making,numerical simulation,online opinion dynamics (OODs),prediction methods
Journal
50
Issue
ISSN
Citations 
10
2168-2216
2
PageRank 
References 
Authors
0.38
27
6
Name
Order
Citations
PageRank
Lei Jiang120.72
Jujun Liu220.38
Dong Zhou3697.35
Qian Zhou420.38
Xiansheng Yang520.38
Guanghui Yu620.38