Title: Topic text detection by clustering algorithm for social network media

Authors: Sha Sha

Addresses: Department of Qi Yue Media, Cangzhou Normal University, Cangzhou, Hebei 061001, China

Abstract: The advent of the internet era has promoted the development of social network media, making the number of people active in these social network platforms greatly increase, and the resulting large amount of data and information makes the fast location retrieval of topics of interest a problem. This paper detected topic texts of social network media by the modified particle swarm optimisation-based K-means (MPSO-means) clustering algorithm to achieve topic text clustering effect and alleviate the problem of inconvenience caused by information overload. The results of the study showed that the clustering results of short texts showed a trend of outperforming long texts; and the MPSO-means algorithm was closer to 1 than the other two algorithms in the values of silhouette coefficient, clustering purity, and homogeneity, with better clustering effect, and also consumed the shortest time in detection, only 1,196 s.

Keywords: text clustering; social network media; topic text; modified particle swarm optimisation-based K-means.

DOI: 10.1504/IJNVO.2024.138480

International Journal of Networking and Virtual Organisations, 2024 Vol.30 No.3, pp.246 - 256

Received: 07 Aug 2023
Accepted: 10 Oct 2023
Published online: 07 May 2024 *

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