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

Author

Listed:
  • Li Ying

    (Jilin University)

  • Li Linlin

    (Jilin University)

  • Li Qianqian

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

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.

Suggested Citation

  • Li Ying & Li Linlin & Li Qianqian, 2022. "The clues in the news media coverage: detecting Chinese collective action trend from a text analytics research framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(2), pages 729-749, April.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:2:d:10.1007_s11135-021-01137-3
    DOI: 10.1007/s11135-021-01137-3
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    2. Mastrorocco, Nicola & Minale, Luigi, 2018. "News media and crime perceptions: Evidence from a natural experiment," Journal of Public Economics, Elsevier, vol. 165(C), pages 230-255.
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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