Shifting attention to accuracy can reduce misinformation online

By Gordon Pennycook, Ziv Epstein, M. Mosleh, Antonio A. Arechar, Dean Eckles, David G. Rand. 2021

937 citations recorded by the source corpus.

Subfields: Communication, Information Systems, Sociology and Political Science, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Gordon Pennycook
2. David G. Rand
3. Yanna Krupnikov
4. Jabin Binnendyk
5. Christie Newton
6. Samara Klar
7. John Ryan
8. Alexandra Sasha Luccioni

In Information Systems

1. David G. Rand
2. Gordon Pennycook
3. Irene Solaiman
4. Marie-Therese Png
5. Zeerak Talat
6. Yacine Jernite
7. Alexandra Sasha Luccioni
8. Jesse Dodge

In Sociology and Political Science

1. David G. Rand
2. Gordon Pennycook
3. Yanna Krupnikov
4. Jabin Binnendyk
5. Christie Newton
6. Samara Klar
7. John Ryan
8. James N. Druckman

In Evaluation & Benchmarks

1. Yanna Krupnikov
2. Irene Solaiman
3. Zeerak Talat
4. David G. Rand
5. Yacine Jernite
6. Alexandra Sasha Luccioni
7. Gordon Pennycook
8. Jesse Dodge

Citations in the corpus

• Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Mitigation and Interventions Interventions on technical systems include encouraging people to shift their attention to the accuracy of posts they might share [180], using crowd-sourced fact checking [90], and using digital forensics to detect AI-generated content [76].”
• A Practical Guide to Doing Behavioral Research on Fake News and Misinformation (2020, background): “Specifically, recent work shows that people often do not consider whether a headline is accurate when they make judgments about whether they would be willing to share it (Pennycook, McPhetres, et al., 2020; Pennycook et al., 2021).”
• The Psychology of Fake News. (2021, background): “Consistent with this proposal, of the false headlines that were shared in the baseline condition of [21], 33% were both believed and shared when participants were asked directly about accuracy – however, this leaves the remaining 67% of sharing unexplained by confusion.”
• (Mis)estimating Affective Polarization (2021, background): “A related approach would be to work with social media companies to implement nudges about politics when it comes to highly politicized news content that may distort perceptions (e.g., Pennycook et al. 2021).”
• Growing polarization around climate change on social media (2021, background): “However, many studies highlight the importance of Twitter as a critical tool for studying climate communication [10, 11], political polarisation [12, 13] , and misinformation [14].”
• Partisan Polarization Is the Primary Psychological Motivation behind Political Fake News Sharing on Twitter (2020, background): “Yet, even if such interventions can drive down sharing of genuine fake news (Pennycook et al. 2021), the underlying problem remains.”