Evidence map›Paper›PMID 40315197›Full record

ArticlePloS one2025

Can deepfakes manipulate us? Assessing the evidence via a critical scoping review.

Didier Ching, John Twomey, Matthew P Aylett, Michael Quayle, Conor Linehan, Gillian Murphy

Abstract readScoping Review
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Didier ChingSchool of Applied Psychology, University College Cork, Cork, Ireland.ORCID https://orcid.org/0000-0001-6475-1119
John TwomeySchool of Applied Psychology, University College Cork, Cork, Ireland.ORCID https://orcid.org/0000-0002-5493-8518
Matthew P AylettCereProc Ltd, Edinburgh, United Kingdom.
Michael QuayleLero the Research Ireland Centre for Software, Limerick, Ireland.
Conor LinehanSchool of Applied Psychology, University College Cork, Cork, Ireland.ORCID https://orcid.org/0000-0002-7654-6697
Gillian MurphySchool of Applied Psychology, University College Cork, Cork, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deepfakes are one of the most recent developments in misinformation technology and are capable of superimposing one person's face onto another in video format. The potential of this technology to defame and cause harm is clear. However, despite the grave concerns expressed about deepfakes, these concerns are rarely accompanied with empirical evidence. We present a scoping review of the existing empirical studies that aim to investigate the effects of viewing deepfakes on people's beliefs, memories, and behaviour. Five databases were searched, producing an initial sample of 2004 papers, from which 22 relevant papers were identified, varying in methodology and research methods used. Overall, we found that the early studies on this topic have often produced inconclusive findings regarding the existence of uniquely persuasive or convincing effects of deepfake exposure. Moreover, many experiments demonstrated poor methodology and did not include a non-deepfake comparator (e.g., text-based misinformation). We conclude that speculation and scare mongering about dystopian uses of deepfake technologies has far outpaced experimental research that assess these harms. We close by offering insights on how to conduct improved empirical work in this area.

Indexed as

CommunicationNeural Networks, ComputerHumans

Identifiers

PMID40315197
PMCPMC12047760

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.