Title: Data mining method of social media hot topics based on time series clustering

Authors: Wei Wang

Addresses: College of Information Engineering, Henan Industry and Trade Vocational College, Zhengzhou, Henan 450053, China

Abstract: In order to overcome the problems of large analysis error and low mining accuracy of traditional hot topic data mining methods, this paper proposes a new social media hot topic data mining method based on time series clustering. Firstly, the topic feedback forms of reading, comment, forwarding and praise are taken as the research objects. Secondly, the contribution value of various data in the topic heat is calculated to obtain the topic heat results. Finally, take the topic value as the goal and follow-up reports as the index to realise data mining. The test results show that the design method can accurately analyse the data of reading, comments, forwarding and likes. The analysis results of the number of follow-up reports have a high degree of fit with the actual results, and have a high mining accuracy, which is close to 100%.

Keywords: time series clustering; social media hot topics; data mining; topic heat; topic value.

DOI: 10.1504/IJWBC.2024.136647

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.153 - 163

Received: 24 Mar 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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