Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation
Lei-Lei Shi
Lu Liu
Yan Wu
Liang Jiang
Muhammad Kazim
Haider Ali
John Panneerselvam
2381/11588181.v1
https://figshare.le.ac.uk/articles/journal_contribution/Human-Centric_Cyber_Social_Computing_Model_for_Hot-Event_Detection_and_Propagation/11588181
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.
2020-03-26 09:46:02
Science & Technology
Technology
Computer Science, Cybernetics
Computer Science
Computational modeling
Social networking (online)
Event detection
Greedy algorithms
Peer-to-peer computing
Social computing
Approximation algorithms
event propagation
human centric
social computing
INFLUENCE MAXIMIZATION