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Scaling up fact-checking using the wisdom of crowds.
Jennifer Allen1, Antonio A Arechar1,2,3, Gordon Pennycook4
1Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA.
Science Advances
|September 13, 2021
Summary
Politically balanced groups of laypeople can effectively identify online misinformation, correlating with professional fact-checker accuracy. This approach offers a scalable solution to combatting fake news.
Area of Science:
- Social Sciences
- Computational Social Science
- Information Science
Background:
- Professional fact-checking is crucial for combating misinformation but faces scalability challenges.
- Allegations of bias against professional fact-checkers can undermine public trust.
- Developing scalable and trusted methods for misinformation identification is essential.
Purpose of the Study:
- To explore the use of politically balanced layperson groups as a scalable solution for misinformation identification.
- To compare the accuracy of layperson ratings with professional fact-checker assessments.
- To investigate factors influencing agreement between laypeople and fact-checkers.
Main Methods:
- Examined 207 news articles flagged by Facebook algorithms.
- Compared accuracy ratings from three professional fact-checkers against ratings from 1128 Americans on Amazon Mechanical Turk.
- Laypeople rated article headlines and ledes, with analysis of politically balanced groups.
Main Results:
- Average ratings from small, politically balanced layperson crowds correlated with professional fact-checker ratings.
- Layperson crowd ratings accurately predicted whether fact-checkers deemed headlines "true".
- Cognitive reflection, political knowledge, and Democratic Party preference positively related to agreement with fact-checkers.
Conclusions:
- Politically balanced layperson crowds offer a viable and scalable method for identifying online misinformation.
- This crowdsourced approach can supplement or provide an alternative to professional fact-checking.
- Understanding cognitive and political factors can enhance the effectiveness of crowdsourced misinformation detection.
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