The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Misinformation on Misinformation: Conceptual and Methodological Challenges
Open accessPublished version of a preprint
Abstract
Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact are overstated. Drawing on research examining the use of big data in social science and reception studies, we identify six misconceptions about misinformation and highlight the conceptual and methodological challenges they raise. The first set of misconceptions concerns the prevalence and circulation of misinformation. First, scientists focus on social media because it is methodologically convenient, but misinformation is not just a social media problem. Second, the internet is not rife with misinformation or news, but with memes and entertaining content. Third, falsehoods do not spread faster than the truth; how we define (mis)information influences our results and their practical implications. The second set of misconceptions concerns the impact and the reception of misinformation. Fourth, people do not believe everything they see on the internet: the sheer volume of engagement should not be conflated with belief. Fifth, people are more likely to be uninformed than misinformed; surveys overestimate misperceptions and say little about the causal influence of misinformation. Sixth, the influence of misinformation on people’s behavior is overblown as misinformation often “preaches to the choir.” To appropriately understand and fight misinformation, future research needs to address these challenges.
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.
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
null (2016) · Philosophy Study · doi:10.17265/2159-5313/2016.09.003 · Cited by 28,317
Kollmuss, A., & Agyeman, J. (2002) · Environmental Education Research · doi:10.1080/13504620220145401 · Cited by 8,866
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 · cited 1×
“The idea that falsehoods spread faster than the truth was amplified and legitimized by an influential article published in Science which found, among others, that “it took the truth about six times as long as falsehood to reach 1,500 people” (Vosoughi et al., 2018, p.3).”
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767
boyd, D., & Crawford, K. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.678878 · Cited by 5,479
Big Data, new epistemologies and paradigm shifts
Kitchin, R. (2014) · Big Data & Society · doi:10.1177/2053951714528481 · Cited by 2,355
The COVID-19 social media infodemic
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-73510-5 · Cited by 2,313
Cohen, S. (2011) · Routledge eBooks · doi:10.4324/9780203828250 · Cited by 2,265
Defining “Fake News”: A typology of scholarly definitions
Tandoc, E. C., Lim, Z. W., & Ling, R. (2018) · Digital Journalism · doi:10.1080/21670811.2017.1360143 · Cited by 2,172
Fake news on Twitter during the 2016 U.S. presidential election
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019) · Science · doi:10.1126/science.aau2706 · Cited by 1,803
Cited by
Van Bavel, J. J., Robertson, C. E., del Rosario, K., Rasmussen, J., & Rathje, S. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022123-110258 · Cited by 121
People think that social media platforms do (but should not) amplify divisive content
Rathje, S., Robertson, C., Brady, W. J., & Van Bavel, J. J. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231190392 · Cited by 59
Pimentel, D. R. (2025) · Journal of Research in Science Teaching · doi:10.1002/tea.21974 · Cited by 28
Metadata
- Authors
- Altay, S., Berriche, M., & Acerbi, A.
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- Published
- 2023-01
- Volume
- 9
- Issue
- 1
- ISSN
- 2056-3051 (ISSN-L)
- OpenAlex
- W4318171182
- Cited by
- 299
- References
- 69
- FWCI
- 130.98
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Misinformation on Misinformation: Conceptual and Methodological Challenges. https://app.cochat.ai/papers/10.1177/20563051221150412
- The paper
- Altay, S., Berriche, M., & Acerbi, A. (2023). Misinformation on Misinformation: Conceptual and Methodological Challenges. Social Media + Society. https://doi.org/10.1177/20563051221150412
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.