Research on Early Warning of Network Public Opinion Based on Bibliometrics

DOI

10.2991/978-94-6463-042-8_173How to use a DOI?

Keywords

network public opinion; early warning; bibliometrics

Abstract

Public opinion early warning research is the "pearl in the crown" of public opinion research in the network era. Network public opinion early warning research has been carried out for 15 years. By combing the 248 articles collected by CNKI we can more comprehensively understand the context and trend of network public opinion early warning research, and better promote the improvement of network public opinion early warning research system. With the help of CiteSpace and CNKI, this paper makes a bibliometric statistical analysis of the number of articles published over the years, the main research forces and the distribution of major journals. Research findings: Domestic research on online public opinion early warning began in 2007, and began to increase in 2011. The main research strength is concentrated in domestic universities, among which the 985 and 211 project universities account for a relatively small proportion. Most of the target literature comes from Library and Information and technical journals.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yan Li
AU  - Zhen Liu
PY  - 2022
DA  - 2022/12/29
TI  - Research on Early Warning of Network Public Opinion Based on Bibliometrics
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 1216
EP  - 1220
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_173
DO  - 10.2991/978-94-6463-042-8_173
ID  - Li2022
ER  -