CoChat
Sign Up
Cited by 156Misinformation and Its ImpactsSocial Media and PoliticsCommunication and COVID-19 Impact

The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.

Misunderstanding the harms of online misinformation

Budak, Ceren, Nyhan, Brendan, Rothschild, David M., Thorson, Emily, Watts, Duncan J.·2024·Nature
DOI 10.1038/s41586-024-07417-w
Open this paper in CoChat — free account

Abstract

The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.

Overview

Integrity signals

Registry

Crossref · 10.1038/s41586-024-07417-w

Cites

98 of 98 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Research works printed but not linked: 7

  1. Tufekci, Z. Algorithmic harms beyond Facebook and google: emergent challenges of computational agency. Colo. Tech. Law J. 13, 203 (2015).
  2. Soral, W., Liu, J. & Bilewicz, M. Media of contempt: social media consumption predicts normative acceptance of anti-Muslim hate speech and Islamo-prejudice. Int. J. Conf. Violence 14, 1–13 (2020).
  3. Muddiman, A., Budak, C., Murray, C., Kim, Y. & Stroud, N. J. Indexing theory during an emerging health crisis: how U.S. TV news indexed elite perspectives and amplified COVID-19 misinformation. Ann. Inte. Commun. Assoc. 46, 174–204 (2022). This paper shows how mainstream media also spreads misinformation through amplification of misleading statements from elites.
  4. Scheuerman, M. K., Jiang, J. A., Fiesler, C. & Brubaker, J. R. A framework of severity for harmful content online. Proc. ACM Hum. Comput. Interact. 5, 1–33 (2021).
  5. Marchal, N., Kollanyi, B., Neudert, L.-M. & Howard, P. N. Junk News during the EU Parliamentary Elections: Lessons from A Seven-language Study of Twitter and Facebook (Univ. Oxford, 2019).
  6. Bryant, L. V. The YouTube algorithm and the alt-right filter bubble. Open Inf. Sci. 4, 85–90 (2020).
  7. Cruz, J. C. B. & Cheng, C. Establishing baselines for text classification in low-resource languages. Preprint at https://arxiv.org/abs/2005.02068 (2020). This paper shows one of the challenges that makes content moderation costlier in less resourced countries. · arXiv

Cited by

Sorted by citations

See all 156

Related

See all

Metadata

Authors
Budak, C., Nyhan, B., Rothschild, D. M., Thorson, E., & Watts, D. J.
Affiliations
  • University of Michigan (US)
  • Dartmouth College (US)
  • Microsoft Research New York City (United States) (US)
  • Syracuse University (US)
  • University of Pennsylvania (US)
Corresponding author
Rothschild, David M.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2024-06-05
Volume
630
Issue
8015
Pages
45-53
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-024-07417-w
OpenAlex
W4399366359
Cited by
156
References
98
Citations by year
2026: 65 · 2025: 71 · 2024: 20
FWCI
16.23
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Communication › Communication and COVID-19 Impact
Keywords
online misinformation, social media, algorithmic amplification, political polarization, platform accountability, platform transparency
MeSH terms
Disinformation, Algorithms, Communication, Humans, Motivation, Internet, Social Media

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