How search engines disseminate information about COVID-19 and why they should do better
Open accessAbstract
Access to accurate and up-to-date information is essential for individual and collective decision making, especially at times of emergency. On February 26, 2020, two weeks before the World Health Organization (WHO) officially declared the COVID-19’s emergency a “pandemic,” we systematically collected and analyzed search results for the term “coronavirus” in three languages from six search
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
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?
A recent study assessed the pattern of information dissemination about COVID 19 in search engines and reported that Google, Bing search engines mainly disseminate information from the legacy media, government, and healthcare information.
“A recent study assessed the pattern of information dissemination about COVID 19 in search engines and reported that Google, Bing search engines mainly disseminate information from the legacy media, government, and healthcare information [56].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Transparent to whom? No algorithmic accountability without a critical audience
Kemper, J., & Kolkman, D. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2018.1477967 · Cited by 260 · cited 1× · see citing works
“Criticism of algorithmic non-transparency in information distribution is not new (Pasquale, 2015; Kemper & Kolkman, 2019; Noble, 2018).”
Background
Flagged first, then by citations
Research works printed but not linked: 14
- Proceedings of the 20th ACM international conference on information and knowledge management (pp. 1933-1936). ACM Press.
- Cutrell, E., & Guan, Z. (2007). What are you looking for? An eye-tracking study of information usage in web search. In Proceedings of the SIGCHI conference on human factors in computing systems- CHI '07 (pp. 407-416). ACM Press.
- Foroohar, R. (2019). Don't be evil: The case against big tech. Penguin UK.
- González-Caro, C., & Marcos, M. C. (2011). Different users and intents: An eye-tracking analysis of web search. In Proceedings of WSDM 11 (pp. 9-12). ACM Press. Google Trends. (2020, April 21). covid-19, coronavirus. Google. https://trends.google.com/trends/explore?q=covid-19,coronavirus
- Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013). Measuring personalization of web search. In Proceedings of the 22nd international conference on World Wide Web (pp. 527-538). ACM Press.
- Harambam, J., Bountouridis, D., Makhortykh, M., & Van Hoboken, J. (2019). Designing for the better by taking users into account: A qualitative evaluation of user control mechanisms in (news) recommender systems. In Proceedings of the 13th ACM conference on recommender systems (pp. 69-77). ACM Press.
- Helberger, N. (2011). Diversity by design. Journal of Information Policy, 1, 441-469.
- Kliman-Silver, C., Hannak, A., Lazer, D., Wilson, C., & Mislove, A. (2015). Location, location, location: The impact of geolocation on web search personalization. In Proceedings of the 2015 internet measurement conference (pp. 121-127). ACM Press.
- Mittelstadt, B. (2016). Automation, algorithms, and politics: Auditing for transparency in content personalization systems. International Journal of Communication, 10: 4991-5002.
- Pasquale, F. (2015). The black box society. Harvard University Press.
- Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
- Puschmann, C. (2019). Beyond the bubble: Assessing the diversity of political search results. Digital Journalism, 7(6), 824-843.
- Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018). Auditing partisan audience bias within Google Search. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW): 1-22.
- Webber, W., Moffat, A., & Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Transactions on Information Systems, 28(4), 1-38.
Cited by
Sorted by citations
Htay, M. N. N., Parial, L. L., Tolabing, M. C., Dadaczynski, K., Okan, O., Leung, A. Y. M., & Su, T. T. (2022) · PLOS ONE · doi:10.1371/journal.pone.0266276 · Cited by 78 · see citing works
- Authors
- Makhortykh, M., Urman, A., & Ulloa, R.
- Affiliations
- Harvard University (US)
- University of Bern (CH)
- Venue
- Harvard Kennedy School Misinformation Review
- Published
- 2020-05-11
- ISSN
- 2766-1652 (ISSN-L)
- OpenAlex
- W3025764718
- Cited by
- 54
- References
- 1
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 9 · 2023: 9 · 2022: 10 · 2021: 14 · 2020: 3
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Harvard Kennedy School Misinformation Review (publishedVersion, cc-by)
- BORIS (University Library Bern) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) (submittedVersion, cc-by)
- Open Access CRIS of the University of Bern
- Funders
- University of Bern
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- COVID-19 pandemic, search engines, search result quality, misinformation, information dissemination, emergency decision-making
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
