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- W4295357257 abstract "Purpose A huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media. Design/methodology/approach This study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions. Findings This paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction. Originality/value This study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics." @default.
- W4295357257 created "2022-09-13" @default.
- W4295357257 creator A5027590471 @default.
- W4295357257 creator A5028128236 @default.
- W4295357257 creator A5043146219 @default.
- W4295357257 creator A5090335612 @default.
- W4295357257 date "2022-09-14" @default.
- W4295357257 modified "2023-09-23" @default.
- W4295357257 title "Detecting the research structure and topic trends of social media using static and dynamic probabilistic topic models" @default.
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