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Fake it till you make it? Individual beliefs on fake news detection
Last registered on April 28, 2021

Pre-Trial

Trial Information
General Information
Title
Fake it till you make it? Individual beliefs on fake news detection
RCT ID
AEARCTR-0007616
Initial registration date
April 28, 2021
Last updated
April 28, 2021 12:37 PM EDT
Location(s)

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Request Information
Primary Investigator
Affiliation
Toulouse School of Economics
Other Primary Investigator(s)
PI Affiliation
Goethe University Frankfurt
Additional Trial Information
Status
In development
Start date
2021-05-01
End date
2021-12-31
Secondary IDs
Abstract
According to recent surveys, despite considering made-up news more problematic than terrorism, illegal immigration, racism or sexism, the vast majority of Americans feel confident in their ability to detect fake news. However, the literature in economics and psychology shows that people hold motivated beliefs about self and ego-relevant aspects, ranging from IQ to attractiveness, which typically lead the average person to exhibit overconfidence about own abilities or characteristics. Motivated by this evidence, our aim is to answer the following question: Do individuals have accurate beliefs about their ability to discern true and false news? Imprecise beliefs about own sensitivity to false news may have non-negligible effects on individual economic behavior, as well as aggregate welfare. To this end, we employ a representative sample of the US population to conduct a survey with an incentivized fake news detection task. This allows us to estimate people's prior beliefs and actual ability to correctly assess the accuracy of the information content of the news presented to them. We also explore whether prior beliefs and actual fake news detection ability are predicted by individual characteristics (e.g. income, education, cognitive reflection) and the intensity of social media use. Finally, we study whether subjects update beliefs, change fake news detection performance, as well as economic decisions, in response to a signal about own true fake news detection ability.
External Link(s)
Registration Citation
Citation
Assenza, Tiziana and Alberto Cardaci. 2021. "Fake it till you make it? Individual beliefs on fake news detection." AEA RCT Registry. April 28. https://doi.org/10.1257/rct.7616-1.1.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2021-05-15
Intervention End Date
2021-05-31
Primary Outcomes
Primary Outcomes (end points)
Estimate people's prior beliefs and actual ability to correctly assess the accuracy of the information content of the news presented to them (Fake News detection ability).
Explore whether prior beliefs and actual Fake News detection ability are predicted by individual characteristics (e.g., income, education, etc.) and the intensity of social media use and addiction.
Study whether subjects update beliefs, change FN detection performance, as well as economic decisions, in response to a signal about own true Fake News detection ability.
Explore people's prior belief and attitudes towards socially and economically relevant domains (e.g., vaccination, climate change) and test the causal effect of FN exposure on the beliefs and attitudes within these domains.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This is an incentivised online survey with a control group and one treatment arm. In the control group, subjects carry out a fake news detection task and we estimate prior and posterior beliefs about their ability to discern true and false news in the task. The treatment mimics the control group, except that subjects receive a signal about their true fake news detection ability while performing the task. In both treatments, subjects are also randomly assigned to two groups: one group sees the source of the news, while the other does not. Finally, for all subjects, we collect information on individual characteristics, ranging from socio-demographics and political orientation, to cognitive sophistication.
Experimental Design Details
Not available
Randomization Method
Computer (online survey)
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
2400 individuals
Sample size: planned number of observations
2400
Sample size (or number of clusters) by treatment arms
600
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Toulouse School of Economics
IRB Approval Date
2021-04-19
IRB Approval Number
N/A