The DOI matches its Crossref record; 156 of the 158 references it cites were found in a registry or an index.
Capturing Clicks: How the Chinese Government Uses Clickbait to Compete for Visibility
Abstract
The proliferation of social media and digital technologies has made it necessary for governments to expand their focus beyond propaganda content in order to disseminate propaganda effectively. We identify a strategy of using clickbait to increase the visibility of political propaganda. We show that such a strategy is used across China by combining ethnography with a computational analysis of a novel dataset of the titles of 197,303 propaganda posts made by 213 Chinese city-level governments on WeChat. We find that Chinese propagandists face intense pressures to demonstrate their effectiveness on social media because their work is heavily quantified–measured, analyzed, and ranked–with metrics such as views and likes. Propagandists use both clickbait and non-propaganda content (e.g., lifestyle tips) to capture clicks, but rely more heavily on clickbait because it does not decrease space available for political propaganda. Government propagandists use clickbait at a rate commensurate with commercial and celebrity social media accounts. The use of clickbait is associated with more views and likes, as well as greater reach of government propaganda outlets and messages. These results reveal how the advertising-based business model and affordances of social media influence political propaganda and how government strategies to control information are moving beyond censorship, propaganda, and disinformation.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“At the same time, social media companies play a problematic role: their algorithms, designed to maximize engagement and revenue, boost polarizing and manipulative content.41”
From The rise of authoritarian informationalism: escalating surveillance, manipulation, and control (2026) · cites this paper as [41]
Citations
Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847
Halliday, M. A. K., & Hasan, R. (2014) · Routledge eBooks · doi:10.4324/9781315836010 · Cited by 7,392
boyd, D., & Crawford, K. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.678878 · Cited by 5,479
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
Journal of Statistical Software
de Leeuw, J. (2009) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.10 · Cited by 3,651
Epistemic cultures: how the sciences make knowledge
(1999) · Choice Reviews Online · doi:10.5860/choice.37-0887 · Cited by 3,347
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Doyle, T. (2017) · The Information Society · doi:10.1080/01972243.2017.1354593 · Cited by 3,111
Feature-rich part-of-speech tagging with a cyclic dependency network
Toutanova, K., Klein, D., Manning, C. D., & Singer, Y. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073478 · Cited by 2,921
Research works printed but not linked: 25
- boyd D., 2011, A networked self: Identity, community, and culture on social network sites (pp. 39–58)
- Biyani P., 2016, Thirtieth AAAI conference on artificial intelligence
- Bourgault A., 2015, Susquehanna University Political Review, 6
- Forelle M., 2015, arXiv Preprint arXiv:1507.07109 · arXiv
- Freedom House, 2017, : Press freedom’s dark horizon. Washington, DC: Freedom House
- Freyburg T., 2018, International Journal of Communication, 12, 3896
- Hem, M. (2014). Evading the censors: Critical journalism in authoritarian states.Reuters Institute Fellowship Paper, University of Oxford, Trinity Term.
- Hilbert M., 2019, International Journal of Communication, 13
- Jowett G. S., 2018, Propaganda & persuasion
- Lu Y., 2018, International communication associations 68th annual conference
- McGrenere J., 2000, Graphics interface, 2000, 179
- Petrovic S., 2011, Fifth international AAAI conference on weblogs and social media
- Ratkiewicz J., 2011, Fifth international AAAI conference on weblogs and social media
- Roberts M. E., 2013, Advances in neural information processing systems workshop on topic models: Computation, application, and evaluation, 1
- Schrock A. R., 2015, International Journal of Communication, 9
- Schwartz B. I., 1970, Communism and China ideology in flux: Ideology in flux
- Shorey S., 2016, International Journal of Communication, 10
- Thelen P. D., 2018, Public Relations Journal, 12
- Tseng H., 2005, Proceedings of the fourth SIGHAN workshop on Chinese language processing
- Tseng H., 2005, Proceedings of the fourth SIGHAN workshop on Chinese language processing
- Wang, Y. & Dickson, B. (2019).How corruption investigations undermine regime support: Evidence from China(SSRN 3086286).
- Woolley S. C., 2016, First Monday, 21
- Xiang A., 2019, Global Media Journal, 6, 170
- Zhang X., 2014, International Journal of Communication, 8, 2
- Zhu T., 2013, Presented as part of the 22nd {USENIX} Security Symposium ({USENIX} Security 13), 227
Cited by
Sorted by citations
Why Does China Allow Freer Social Media? Protests versus Surveillance and Propaganda
Qin, B., Strömberg, D., & Wu, Y. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.1.117 · Cited by 353
A systematic review of worldwide causal and correlational evidence on digital media and democracy
Lorenz-Spreen, P., Oswald, L., Lewandowsky, S., & Hertwig, R. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01460-1 · Cited by 348
Earl, J. (2022) · Science Advances · doi:10.1126/sciadv.abl8198 · Cited by 250
Self-citationUncovering the Truth about Fake News: A Research Model Grounded in Multi-Disciplinary Literature
George, J. J., Gerhart, N., & Torres, R. (2021) · Journal of Management Information Systems · doi:10.1080/07421222.2021.1990608 · Cited by 93
Restyling propaganda: popularized party press and the making of soft propaganda in China
Zou, S. (2021) · Information Communication & Society · doi:10.1080/1369118x.2021.1942954 · Cited by 83
Metadata
- Authors
- Lu, Y., & Pan, J.
- Affiliations
- Stanford University (US)
- Corresponding author
- Pan, Jennifer
- Venue
- Political Communication
- Publisher
- Informa UK Limited
- Published
- 2020-07-20
- Volume
- 38
- Issue
- 1-2
- Pages
- 23-54
- ISSN
- 1058-4609 (ISSN-L)
- 1091-7675
- OpenAlex
- W3045397880
- Cited by
- 130
- References
- 158
- Citations by year
- 2026: 18 · 2025: 26 · 2024: 22 · 2023: 40 · 2022: 14 · 2021: 9
- FWCI
- 21.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Political Communication (publishedVersion)
- Funders
- National Science Foundation (1831481)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Communication › Social Media and Politics
- Keywords
- clickbait, political propaganda, state propaganda, WeChat, social media visibility, information control, censorship, disinformation
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review