Exploring Evolution of Public Opinions on Tianya Club Using Dynamic Topic Models

Yan Zhihua () and Tang Xijin ()
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Yan Zhihua: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
Tang Xijin: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

Journal of Systems Science and Information, 2020, vol. 8, issue 4, 309-324

Abstract: Online media have brought tremendous changes to civic life, public opinions, and government administration. Compared with traditional media, online media not only allow individuals to browse news and express their views more freely, but also accelerate the transmission of opinions and expand influence. As public opinions may arouse societal unrest, it is worth detecting the primary topics and uncovering the evolution trends of public opinions for societal administration. Various algorithms are developed to deal with the huge volume of unstructured online media data. In this study, dynamic topic model is employed to explore topic content evolution and prevalence evolution using the original posts published from 2013 to 2017 on the Tianya Zatan Board of Tianya Club, which is one of the most popular BBS in China. Based on semantic similarities, topics are grouped into three themes: Family life, societal affairs, and government administration. The evolution of topic prevalence and content are affected by emergent incidents. Topics on family life become popular, while themes “societal affairs” and “government administration” with bigger standard deviations are more likely to be influenced by emergent hot events. Content evolution represented by monthly pairwise distance matrix is very easy to find change points of topic content.

Keywords: topic modeling; dynamic topic models; text mining; topic evolution (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:8:y:2020:i:4:p:309-324:n:2

DOI: 10.21078/JSSI-2020-309-16

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