Information Interventions about Deepfakes and their Impact on News Consumption

Last registered on October 09, 2025
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Pre-Trial

Trial Information

General Information

Title
Information Interventions about Deepfakes and their Impact on News Consumption
RCT ID
AEARCTR-0016741
Initial registration date
September 25, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 29, 2025, 10:57 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
October 09, 2025, 2:02 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Germany
Region

Primary Investigator

Name
Ananya Sen
Affiliation
Carnegie Mellon University
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Other Primary Investigator(s)

PI Name
Filipe Campante
PI Affiliation
Johns Hopkins University
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PI Name
Ruben Durante
PI Affiliation
NUS
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PI Name
Felix Hagemeister
PI Affiliation
Süddeutsche Zeitung Digitale Medien
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Additional Trial Information

Status
Completed
Start date
2025-09-12
End date
2025-10-01
Keywords
Firms & Productivity, Other
Additional Keywords
AI, Online News, Deepfakes, News consumption, Field Experiment
JEL code(s)
D91, L82
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study aims to understand the impact of providing information about the potential of AI to generate misinformation (deepfakes) on news consumption and subscription outcomes. In particular, the key idea is that highlighting the ability of AI to generate seemingly real content encourages news consumers to engage more with a trusted outlet. We partner with a large news outlet in Germany to test this by encouraging users to engage with custom-created content. In particular, as part of the field experiment, we utilize the mailing list of our partner news organization. We use approximately 150,000 subscribers as part of the experiment, who are then randomly split into three groups. In the treatment group, readers gain access to an AI literacy article that highlights ways to identify deepfake videos better. In this process, readers are encouraged to take a quiz that tests their ability to identify AI-generated videos and based on each question, the article will provide dimensions of videos that readers should look out for.

This intervention will test an active approach that can be taken by a news outlet to combat technology-enabled misinformation. The direction and magnitude of the results can have an important impact on how we design policies to combat misinformation and have individuals engage with high-quality news, which in turn provides incentives to news outlets to keep investing in writing stories with important societal implications.

As a benchmark to quantify the treatment effects, we will also have two control conditions. In the first control condition, we will email another group a link to a different, yet comparable, news story from SZ. This will account for changes in browsing and subscription behavior resulting from an email from the news organization itself. A final control group will receive no email, and we will track their browsing and subscription behavior as a "pure control" group.
External Link(s)

Registration Citation

Citation
Campante, Filipe et al. 2025. "Information Interventions about Deepfakes and their Impact on News Consumption." AEA RCT Registry. October 09. https://doi.org/10.1257/rct.16741-1.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We use approximately 150,000 subscribers as part of the experiment, who are then randomly split into three groups. In the treatment group, readers gain access to an AI literacy article that highlights ways to identify deepfake videos better as they go through a quiz.

As a benchmark to quantify the treatment effects, we will also have two control conditions. In the first control condition, we will email a group a link to a different SZ news story. This will account for changes in browsing and subscription behavior resulting from an email from the news organization itself. A final control group will receive no email, and we will track their browsing and subscription behavior as a "pure control" group.
Intervention (Hidden)
Intervention Start Date
2025-09-26
Intervention End Date
2025-10-01

Primary Outcomes

Primary Outcomes (end points)
(1) Browsing on the SZ platform.
(2) Subscribing to SZ.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We use approximately 150,000 subscribers as part of the experiment, who are then randomly split into three groups. In the treatment group, readers gain access to an AI literacy article that highlights ways to identify deepfake videos better. They will be guided through different techniques to spot deepfakes as they are encouraged to take a quiz that tests their ability to identify AI-generated videos.

We will also have two control conditions. In the first control condition, we will email a group of individuals on the mailing list a link to a different SZ news story. This will account for changes in browsing and subscription behavior resulting from an email from the news organization itself. A final control group will receive no email, and we will track their browsing and subscription behavior as a "pure control" group.

We will employ a between-subjects design, such that an individual on the mailing list will be assigned to one of the treatments. The main outcome variables of interest will be their subsequent browsing and subscription behavior.

We will also explore heterogeneity along two dimensions: (1) their prior news consumption and (2) their prior knowledge based on topics they consume on the platform
Experimental Design Details
Randomization will be done with a computer software.
Randomization Method
Randomization will be done with a computer software.
Randomization Unit
Individual reader
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
--
Sample size: planned number of observations
About 150,000 -- this could be subject to some change based on survey uptake and traffic allocation by the company.
Sample size (or number of clusters) by treatment arms
50,000
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials