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Influencing overseas Chinese by tweets: text-images as the key tactic of Chinese propaganda

Author

Listed:
  • Austin Horng-En Wang

    (University of Nevada)

  • Mei-chun Lee

    (University of California)

  • Min-Hsuan Wu

    (Doublethink Lab)

  • Puma Shen

    (National Taipei University)

Abstract

The literature on China’s social media foreign propaganda mostly focuses on text-format contents in English, which may miss the real target and the tool for analysis. In this article, we traced 1256 Twitter accounts echoing China government’s #USAVirus propaganda before and after Twitter removed state-linked operations on June 12, 2020. The 3567 tweets with #USAVirus we collected, albeit many written in English, 74% of them attached with a lengthy simplified Chinese text-image. Distribution of the post-creation time fits the working-hour in China. Overall, 475 (37.8%) accounts we traced were later suspended after Twitter’s disclosure. Our dataset enables us to analyze why and why not Twitter suspends certain accounts. We apply the decision tree, random forest, and logit regression to explain the suspensions. All models suggest that the inclusion of a text-image is the most important predictor. The importance outweighs the number of followers, engagement, and the text content of the tweet. The prevalence of simplified Chinese text-images in the #USAVirus trend and their impact on Twitter account suspensions both evidence the importance of text-image in the study of state-led propaganda. Our result suggests the necessity of extracting and analyzing the content in the attached text-image.

Suggested Citation

  • Austin Horng-En Wang & Mei-chun Lee & Min-Hsuan Wu & Puma Shen, 2020. "Influencing overseas Chinese by tweets: text-images as the key tactic of Chinese propaganda," Journal of Computational Social Science, Springer, vol. 3(2), pages 469-486, November.
    • Handle: RePEc:spr:jcsosc:v:3:y:2020:i:2:d:10.1007_s42001-020-00091-8
    DOI: 10.1007/s42001-020-00091-8
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    References listed on IDEAS

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    1. Hobbs, William R. & Roberts, Margaret E., 2018. "How Sudden Censorship Can Increase Access to Information," American Political Science Review, Cambridge University Press, vol. 112(3), pages 621-636, August.
    2. King, Gary & Pan, Jennifer & Roberts, Margaret E., 2017. "How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument," American Political Science Review, Cambridge University Press, vol. 111(3), pages 484-501, August.
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    Cited by:

    1. Emilio Ferrara & Stefano Cresci & Luca Luceri, 2020. "Misinformation, manipulation, and abuse on social media in the era of COVID-19," Journal of Computational Social Science, Springer, vol. 3(2), pages 271-277, November.
    2. Ning Xiang & Limao Wang & Shuai Zhong & Chen Zheng & Bo Wang & Qiushi Qu, 2021. "How Does the World View China’s Carbon Policy? A Sentiment Analysis on Twitter Data," Energies, MDPI, vol. 14(22), pages 1-17, November.
    3. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.
    4. Chang, Ho-Chun Herbert & Wang, Austin Horng-En & Fang, Yu Sunny, 2023. "US-Skepticism: Misinformation and Transnational Conspiracy in the 2024 Taiwanese Presidential Elections," OSF Preprints uefgw, Center for Open Science.

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