Understanding and Reducing the Spread of Misinformation Online: Evidence from Lab and Field Experiments

• Activity Number: 283 - Statistical Analysis on Social Media Misinformation Campaigns
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Social Statistics Section
• Author(s): Gordon Pennycook and Ziv Epstein and Mohsen Mosleh and Antonio A. Arechar and Dean Eckles* and David G. Rand
• Companies: University of Regina and Massachusetts Institute of Technology and University of Exeter Business School and Massachusetts Institute of Technology and Massachusetts Institute of Technology and Massachusetts Institute of Technology
• Keywords: social networks; misinformation; social media; field experiments; randomization inference; peer effects

Abstract

Why do people share false and misleading news content on social media, and what can be done about it? In a first survey experiment, we demonstrate a disconnect between accuracy judgments and sharing intentions: Even though true headlines are rated as much more accurate than false headlines, headline veracity has little impact on sharing. However, most people do not want to spread misinformation, but the social media context focuses their attention on factors other than truth and accuracy. Indeed, when directly asked, most participants say it is important to only share news that is accurate. Accordingly, across four survey experiments (total N>3,000) and digital field experiments on Twitter, we find that subtly inducing people to think about accuracy increases the quality of the news they subsequently share. The field experiments make use of granular blocking and Fisherian randomization inference.