Open Access

ARTICLE

Identifying Event-Specific Opinion Leaders by Local Weighted LeaderRank

Wanxia Yang^1,*, Sadaqatur Rehman², Wenhui Que³

1 Mechanical and Electrical Engineering college, Gansu Agricultural University, Lanzhou, 730070, China
2 Department of Computer Science, Namal Institute, Mianwali, 42250, Pakistan
3 Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China

* Corresponding Author: Wanxia Yang. Email:

Intelligent Automation & Soft Computing 2020, 26(6), 1561-1574. https://doi.org/10.32604/iasc.2020.012480

Abstract

Identifying event-specific opinion leaders is essential for understanding event developments and influencing public opinion. News articles are informative and formal in expression, and include valuable information on specific events. In this paper, we propose an improved variant of LeaderRank, called local weighted LeaderRank, to measure the event-specific influence of person nodes in a weighted and undirected person cooccurrence network constructed using news articles related to a specific event. Our proposed method measures the influence of person nodes by considering both the cooccurrence strength between persons, and additional local link weight information for each local person node. To evaluate the performance of our method, we use the weighted susceptible infected (WSI) model to simulate the influence-spreading process in real-person cooccurrence networks. The experiment results obtained after measuring the rank correlations between the rank list generated by the simulation results and those generated by the influence measures show that our method identifies event-specific opinion leaders effectively and performs better than other state-of-the-art influence measures, such as weighted K-shell decomposition and the weighted local centrality.

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Citations