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The clues in the news media coverage: detecting Chinese collective action trend from a text analytics research framework

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Abstract

With the adjustment of social relations and interest patterns brought about by the comprehensive deepening reform in China, new and old contradictions are intertwined, various risks are increased, collective actions occasionally occur, and some new trends are observed. However, due to there is no authoritative database of collective action in China, it is difficult to observe the trend of collective actions. There has been significant research show that news coverage is an effective way to obtain collective action information. Thus, we examine the recent news coverage shift in terms of collective action. We collected 5354 news coverages from 2014 to 2018. Then, we constructed a collective action domain-specific word dictionary and presented a method to automatically detect temporal, spatial, and topical trends of collective action. The proposed framework is based on text mining analysis that collects data from news outlets and extracts valuable data for perceiving the collective action trends. The results show that the proposed method is an effective tool to identify the trends in collective action via machine learning.

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Acknowledgements

The authors acknowledge financial support from the National Natural Science Foundation of China (No. 71774154,72074205,71573247).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchuan, China

    Li Ying & Li Linlin

  2. Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China

    Li Qianqian

  3. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China

    Li Qianqian

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  1. Li Ying
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  2. Li Linlin
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  3. Li Qianqian
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Correspondence to Li Qianqian.

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I certify that this manuscript is original and has not been published and will not be submitted elsewhere for publication while being considered by Quality & Quantity. And the study is not split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time. No data have been fabricated or manipulated (including images) to support your conclusions. No data, text, or theories by others are presented as if they were our own. The submission has been received explicitly from all co-authors. And authors whose names appear on the submission have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results.

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Ying, L., Linlin, L. & Qianqian, L. The clues in the news media coverage: detecting Chinese collective action trend from a text analytics research framework. Qual Quant 56, 729–749 (2022). https://doi.org/10.1007/s11135-021-01137-3

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