Big-Data Based Analysis for Communication Effect of Science-Technology Public Accounts on Social Media

Jinluan Ren, Wen Cao, Bo Li, Lihua Liu, Lin Cai and Ruben Xing
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Jinluan Ren: School of Economics and Management, Communication University of China, Beijing, China
Wen Cao: China Mobile Group Beijing Co., Ltd., Beijing, China
Bo Li: School of Science, Communication University of China, Beijing, China
Lihua Liu: School of Economics and Management, Communication University of China, Beijing, China
Lin Cai: Communication University of China, Beijing, China
Ruben Xing: Montclair State University, Montclair, USA

International Journal of Information Systems in the Service Sector (IJISSS), 2019, vol. 11, issue 3, 56-69

Abstract: Public accounts on social media have become important channels for information dissemination. Well-designed public social media accounts are vital to better communicate science and technology (S-T) achievements. This article defines the S-T communication concept and proposes the analyzing dimensions. In order to measure the communication effect, this research collected 7,246 articles from S-T public accounts on WeChat. We analysis these massive data incorporating neural network (NN) and multivariate linear regression (MLR) model. The evaluation indicator system of communication effect includes three levels indicators. The research found the following factors affecting the S-T communication effect in different degrees: the number of active fans on Science Technology Public Accounts on Social Media (STPA-SM), locations where the articles are published, the authentication status of STPA-SM, and so on. Finally, the article proposes some strategic suggestions for improving the communication effects of S-T achievements through STPA-SM.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jisss0:v:11:y:2019:i:3:p:56-69

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