posted on 2020-03-26, 09:46authored byLei-Lei Shi, Lu Liu, Yan Wu, Liang Jiang, Muhammad Kazim, Haider Ali, John Panneerselvam
Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.
Funding
The research presented in this paper is supported by the National Key R&D Program of China (No. 2017YFE0117500) , the Natura l Science Foundation of Jiangsu Province under Grant BK20170069, and UK - Jiangsu 20 - 20 World Class University Initiative program , and UK - Jiangsu 20 - 20 Initiative Pump Priming Grant .
History
Citation
IEEE Transactions on Computational Social Systems, 2019, vol. 6, no. 5, pp. 1042-1050
Author affiliation
Department of Informatics
Version
AM (Accepted Manuscript)
Published in
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
Volume
6
Issue
5
Pagination
1042 - 1050 (9)
Publisher
Institute of Electrical and Electronics Engineers (IEEE) for 1. IEEE Computer Society 2. IEEE Systems, Man, and Cybernetics Society