Evidence map›Paper›PMID 37596384›Full record

ArticleScientific reports2023

Deepfakes and scientific knowledge dissemination.

Christopher Doss, Jared Mondschein, Dule Shu, Tal Wolfson, Denise Kopecky, Valerie A Fitton-Kane, Lance Bush, Conrad Tucker

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
33.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 57 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Christopher DossRAND Corporation, Santa Monica, USA. cdoss@rand.org.
Jared MondscheinRAND Corporation, Santa Monica, USA.
Dule ShuCarnegie Mellon University, Pittsburgh, USA.
Tal WolfsonPardee RAND Graduate School, Santa Monica, USA.
Denise KopeckyChallenger Center, Washington, D.C., USA.
Valerie A Fitton-KaneChallenger Center, Washington, D.C., USA.
Lance BushChallenger Center, Washington, D.C., USA. lbush@challenger.org.
Conrad TuckerCarnegie Mellon University, Pittsburgh, USA. conradt@andrew.cmu.edu.
Millennium Challenge Corporation · USCarnegie Mellon University · USRAND Corporation · USFrederick S. Pardee RAND Graduate School · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Science misinformation on topics ranging from climate change to vaccines have significant public policy repercussions. Artificial intelligence-based methods of altering videos and photos (deepfakes) lower the barriers to the mass creation and dissemination of realistic, manipulated digital content. The risk of exposure to deepfakes among education stakeholders has increased as learners and educators rely on videos to obtain and share information. We field the first study to understand the vulnerabilities of education stakeholders to science deepfakes and the characteristics that moderate vulnerability. We ground our study in climate change and survey individuals from five populations spanning students, educators, and the adult public. Our sample is nationally representative of three populations. We found that 27-50% of individuals cannot distinguish authentic videos from deepfakes. All populations exhibit vulnerability to deepfakes which increases with age and trust in information sources but has a mixed relationship with political orientation. Adults and educators exhibit greater vulnerability compared to students, indicating that those providing education are especially susceptible. Vulnerability increases with exposure to potential deepfakes, suggesting that deepfakes become more pernicious without interventions. Our results suggest that focusing on the social context in which deepfakes reside is one promising strategy for combatting deepfakes.

Indexed as

Artificial IntelligenceStudentsAdultClimate ChangeEducational StatusHumansInformation Sources

Identifiers

PMID37596384
PMCPMC10439167
OpenAlexW4385980672

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.