Detecting information source in user-text coupled network: cases from Sina Weibo

Yanbing Zhang; Zhiyuan Liu; Yinghong Ma; Shuo Ma

doi:10.1117/12.3038085

7 August 2024 Detecting information source in user-text coupled network: cases from Sina Weibo

Yanbing Zhang, Zhiyuan Liu, Yinghong Ma, Shuo Ma

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292N (2024) https://doi.org/10.1117/12.3038085
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Despite persistent efforts have been made in untangling the information source on complex network, a little attention is investigated for the relation of the underlying information spreading networks and information source detection. Here, we first exploit the relationship between users and the information spread among them, constructing a user-text coupled network based on two independent networks, which are users' network and texts' similarity network respectively. After that, an algorithm for information source detection based on bread-first search (ISD-BFS) is proposed, in which the spread centrality of nodes is calculated. Theoretical proof is also given that the probability of a node being the information source is proportional to the value of node's spread centrality. Experiments on the real-world data collected from Sina Weibo indicate the feasibility and effectiveness of the proposed algorithm compared with six other state-of-art methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanbing Zhang, Zhiyuan Liu, Yinghong Ma, and Shuo Ma "Detecting information source in user-text coupled network: cases from Sina Weibo", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292N (7 August 2024); https://doi.org/10.1117/12.3038085

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KEYWORDS

Detection and tracking algorithms

Web 2.0 technologies

Roentgenium

Social networks

Computer simulations

Error analysis

Neural networks

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