@article{3a390520a0a9420cacd66e1a666be453,
title = "A digital media literacy intervention increases discernment between mainstream and false news in the United States and India",
abstract = "Widespread belief in misinformation circulating online is a critical challenge for modern societies. While research to date has focused on psychological and political antecedents to this phenomenon, few studies have explored the role of digital media literacy shortfalls. Using data from preregistered survey experiments conducted around recent elections in the United States and India, we assess the effectiveness of an intervention modeled closely on the world's largest media literacy campaign, which provided {"}tips{"} on how to spot false news to people in 14 countries. Our results indicate that exposure to this intervention reduced the perceived accuracy of both mainstream and false news headlines, but effects on the latter were significantly larger. As a result, the intervention improved discernment between mainstream and false news headlines among both a nationally representative sample in the United States (by 26.5%) and a highly educated online sample in India (by 17.5%). This increase in discernment remained measurable several weeks later in the United States (but not in India). However, we find no effects among a representative sample of respondents in a largely rural area of northern India, where rates of social media use are far lower.",
keywords = "Digital literacy, Misinformation, Social media",
author = "Guess, {Andrew M.} and Michael Lerner and Benjamin Lyons and Montgomery, {Jacob M.} and Brendan Nyhan and Jason Reifler and Neelanjan Sircar",
note = "Funding Information: ACKNOWLEDGMENTS. We thank Democracy Fund, the European Research Council under the European Union{\textquoteright}s Horizon 2020 research and innovation program (Grant 682758), and the Facebook Integrity Foundational Research Awards for funding support and Sumit Parashar for outstanding research assistance. We are also grateful to Calvin Lai, Ben Kaiser, Ro{\textquoteright}ee Levy, Jonathan Mayer, Kevin Munger, Yusuf Neggers, Christine Stedtnitz, Emily Thorson, Tim Vercellotti, Shun Yamaya, Dannagal Young, and Thomas Zeitzoff; to Henrik Oscarsson, Jesper Stromback, and the “Knowledge Resistance” group at the University of Gothenburg; and to seminar participants at Brigham Young University, George Washington University, Harvard Kennedy School, Katholieke Universiteit Leuven, and Stanford University for helpful comments. We thank YouGov, Morsel Research and Development, and the Internet Research Bureau for assistance administering the surveys. The India study was supported by funding from Face-book, but the company and its employees played no role in how the study was designed, conducted, or analyzed. All conclusions and any errors are our own. Funding Information: We thank Democracy Fund, the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant 682758), and the Facebook Integrity Foundational Research Awards for funding support and Sumit Parashar for outstanding research assistance. We are also grateful to Calvin Lai, Ben Kaiser, Ro'ee Levy, Jonathan Mayer, Kevin Munger, Yusuf Neggers, Christine Stedtnitz, Emily Thorson, Tim Vercellotti, Shun Yamaya, Dannagal Young, and Thomas Zeitzoff; to Henrik Oscarsson, Jesper Stromback, and the {"}Knowledge Resistance{"} group at the University of Gothenburg; and to seminar participants at Brigham Young University, George Washington University, Harvard Kennedy School, Katholieke Universiteit Leuven, and Stanford University for helpful comments. We thank YouGov, Morsel Research and Development, and the Internet Research Bureau for assistance administering the surveys. The India study was supported by funding from Facebook, but the company and its employees played no role in how the study was designed, conducted, or analyzed. All conclusions and any errors are our own. Publisher Copyright: {\textcopyright} 2020 National Academy of Sciences. All rights reserved.",
year = "2020",
month = jul,
day = "7",
doi = "10.1073/pnas.1920498117",
language = "English (US)",
volume = "117",
pages = "15536--15545",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "National Academy of Sciences",
number = "27",
}