Foundations and knowledge clusters in TikTok (Douyin) research: evidence from bibliometric and topic modelling analyses

Abstract

The goal of this study is to comprehensively analyze the dynamics and structure of TikTok research since its initial development. The scholarly composition of articles dealing with TikTok was dissected via a bibliometric study based on a corpus of 542 journal articles from the Scopus database. The results show that TikTok research has flourished in recent years and also demonstrate that the authors’ collaboration networks are disjointed, indicating a lack of cooperation among TikTok researchers. Furthermore, the analysis reveals that research collaboration among academic institutions reflects the North-South divide, also highlighting a limited research collaboration between institutions in developed and developing countries. Based on the keyword co-occurrence network and topic modeling, TikTok research revolves mainly around five thematic areas, including public health, health communication and education, platform governance, body image, and its impact on children and students. Based on these findings, numerous suggestions for further research are offered. As far as the authors are aware, this is the first application of bibliometrics and topic modeling to assess the growth of TikTok research and reveal the intellectual base of this knowledge domain.

Blockchain technology in the renewable energy sector: A co-word analysis of academic discourse

“…As a particular approach within bibliometrics, co-word analysis hinges on the principle of co-occurrence, which is the simultaneous appearance of words within the same document [ 104 , 105 ]. This method facilitates the linking of literature content into a web of knowledge elements via networks formed by these co-occurring words [ 106 ]. It is a common practice for authors to select a set of keywords that best encapsulate their article's primary research theme.…”