Title: Data analysis algorithms for mining online communities from microblogs

Authors: Hongfei Xiao; Suting Zhou; Min Zhao

Addresses: Chuzhou Vocational and Technical College, No. 2188 Fengle Avenue, Chuzhou City, Anhui, 239000, China ' Chuzhou Vocational and Technical College, No. 2188 Fengle Avenue, Chuzhou City, Anhui, 239000, China ' Department of Electronic Engineering, Hebi Automotive Engineering Professional College, Hebi, Henan 458030, China

Abstract: Mining microblog data based on complex networks is conducive to the effective mining of useful information. This paper focuses on community mining. A complex network is introduced, followed by a community mining algorithm based on user similarity. Based on the similarity, different communities were divided, and experiments were carried out with real datasets. The experimental results showed that the accuracy of the algorithm was 87.5%, the recall rate was 87.1% and the operation time was 2.1 s. In the result of dataset 2, the average modularity of the designed algorithm was 0.532, which was better than the Girvan and Newman (GN) algorithm and there was no weak community structure, showing that the algorithm had better performance in community mining. The experimental results demonstrate the reliability of the mining algorithm and clarify the contributions of data mining for detecting communities from a microblog network.

Keywords: community mining; microblog network; social network; web-based communities; data analysis.

DOI: 10.1504/IJWBC.2020.107158

International Journal of Web Based Communities, 2020 Vol.16 No.2, pp.211 - 221

Received: 20 Sep 2019
Accepted: 18 Oct 2019

Published online: 29 Apr 2020 *

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