The DOI matches its Crossref record.
Computational propaganda: Concepts, methods, and challenges
Open accessAbstract
In this dialogue, Phillip Howard introduces “computational propaganda” as an emerging communication tool in political communication and a perspective for investigating misinformation and disinformation. By articulating the concepts, patterns, and mechanisms of computational propaganda, Howard proposes a socio-technical framework for studying computational propaganda. He calls for mixed methods to undertake computational research alongside qualitative investigation, thus addressing the computational as well as the political. Howard emphasizes the battle against algorithm bias, manipulation, and misinformation, and he advocates building an International Panel on the Information Environment (IPIE), an international scientific collaboration, to respond to the challenges. In addition, Howard offers advice on further research in computational propaganda.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Not only in China, but mass surveillance technologies such as facial recognition are increasingly deployed to monitor populations across the globe.”
From The rise of authoritarian informationalism: escalating surveillance, manipulation, and control (2026) · cites this paper as [30]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 5
Cited by
Sorted by citations
The rise of authoritarian informationalism: escalating surveillance, manipulation, and control
Garbe, L., & Maerz, S. F. (2026) · Democratization · doi:10.1080/13510347.2025.2579105 · Cited by 11
Metadata
- Authors
- Howard, P., Lin, F., & Tuzov, V.
- Affiliations
- University of Oxford (GB)
- City University of Hong Kong (HK)
- Venue
- Communication and the Public
- Publisher
- SAGE Publications
- Published
- 2023-06
- Volume
- 8
- Issue
- 2
- Pages
- 47-53
- ISSN
- 2057-0473 (ISSN-L)
- 2057-0481
- OpenAlex
- W4384523610
- Cited by
- 18
- References
- 0
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 3
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Communication and the Public (publishedVersion, cc-by-nc)
- CityU Scholars (submittedVersion, cc-by-nc)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- computational propaganda, misinformation, disinformation, political communication, socio-technical framework, algorithmic bias, information manipulation, mixed methods, international scientific collaboration
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