The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument
Open accessAbstract
The Chinese government has long been suspected of hiring as many as 2 million people to surreptitiously insert huge numbers of pseudonymous and other deceptive writings into the stream of real social media posts, as if they were the genuine opinions of ordinary people. Many academics, and most journalists and activists, claim that these so-called 50c party posts vociferously argue for the government’s side in political and policy debates. As we show, this is also true of most posts openly accused on social media of being 50c. Yet almost no systematic empirical evidence exists for this claim or, more importantly, for the Chinese regime’s strategic objective in pursuing this activity. In the first large-scale empirical analysis of this operation, we show how to identify the secretive authors of these posts, the posts written by them, and their content. We estimate that the government fabricates and posts about 448 million social media comments a year. In contrast to prior claims, we show that the Chinese regime’s strategy is to avoid arguing with skeptics of the party and the government, and to not even discuss controversial issues. We show that the goal of this massive secretive operation is instead to distract the public and change the subject, as most of these posts involve cheerleading for China, the revolutionary history of the Communist Party, or other symbols of the regime. We discuss how these results fit with what is known about the Chinese censorship program and suggest how they may change our broader theoretical understanding of “common knowledge” and information control in authoritarian regimes.
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“tipping points in norm dynamics present both opportunities, such as addressing global challenges (28, 29), and risks, particularly if exploited for social control (76).”
From Emergent social conventions and collective bias in LLM populations (2025) · cites this paper as [76]
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“Oftentimes, this is done with the help of paid users (Han, 2015; King etal., 2017).”
From How Dictators Control the Internet: A Review Essay (2020) · cites this paper as King et al., 2017
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“Censorship in China is driven more by a post’s capacity to stir collective action than otherwise offensive content (132).”
From Physical and virtual repertoires of contention (2022) · cites this paper as [132]
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Metadata
- Authors
- KING, G., PAN, J., & ROBERTS, M. E.
- Affiliations
- Harvard University (US)
- Stanford University (US)
- University of California San Diego (US)
- Corresponding author
- KING, GARY
- PAN, JENNIFER
- ROBERTS, MARGARET E.
- Venue
- American Political Science Review
- Publisher
- Cambridge University Press (CUP)
- Published
- 2017-07-27
- Volume
- 111
- Issue
- 3
- Pages
- 484-501
- ISSN
- 0003-0554 (ISSN-L)
- 1537-5943
- OpenAlex
- W2741445211
- Cited by
- 1,028
- References
- 67
- Citations by year
- 2026: 60 · 2025: 111 · 2024: 115 · 2023: 141 · 2022: 158 · 2021: 118 · 2020: 114 · 2019: 84 · 2018: 84 · 2017: 38
- FWCI
- 224.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- www.cambridge.org/core/terms (unspecified)
- Locations
- American Political Science Review (publishedVersion, public-domain)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- Chinese government, social media manipulation, authorship attribution, censorship, information control, authoritarian regimes, common knowledge, communist parties, cheerleading
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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