The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings - model
- Used a Bayesian model to track headlines tagged with warnings based on the probability that a headline is fact-checked and the probability that fact-checkers did not correctly identify headlines with false information
- Used Bayes’ rule to calculate changes in an individual’s perceptions when they see a headline with or without warnings
- Since researchers found that a warning effect decreases people’s beliefs when they see a warning and the implied truth effect leads them to believe in the accuracy of the information when there are no warnings, they proceeded with two experiments to study these two effects in depth
- To verify the implied truth effect, researchers decided to run a separate experiment by adding verification tags to compare findings to the experiment with headlines containing warning vs. no warning
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CSCW (Computer-supported cooperative work)
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The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings
The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings - model
The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings - discussion
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The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings - Study 1 Data Collection and Analysis
The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings - Study 2 Data Collection and Analysis