Fighting misinformation on social media using crowdsourced judgments of news source quality

By Gordon Pennycook, David G. Rand. Proceedings of the National Academy of Sciences of the United States of America, 2019

817 citations recorded by the source corpus.

Significance Many people consume news via social media. It is therefore desirable to reduce social media users’ exposure to low-quality news content. One possible intervention is for social media ranking algorithms to show relatively less content from sources that users deem to be untrustworthy. But are laypeople’s judgments reliable indicators of quality, or are they corrupted by either partisan bias or lack of information? Perhaps surprisingly, we find that laypeople—on average—are quite good at distinguishing between lower- and higher-quality sources. These results indicate that incorporating the trust ratings of laypeople into social media ranking algorithms may prove an effective intervention against misinformation, fake news, and news content with heavy political bias. Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platform algorithms preferentially display content from news sources that users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (n = 1,010 from Mechanical Turk and n = 970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: (i) mainstream media outlets, (ii) hyperpartisan websites, and (iii) websites that produce blatantly false content (“fake news”). Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyperpartisan or fake news sources. Although this difference was larger for Democrats than Republicans—mostly due to distrust of mainstream sources by Republicans—every mainstream source (with one exception) was rated as more trustworthy than every hyperpartisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r = 0.90) with ratings provided by professional fact-checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low- and high-quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up-rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9861c0c52c8c5c933983d9fb2dbb9381eb393f04

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Citations in the corpus

Political Ideology Predicts Perceptions of the Threat of COVID-19 (and Susceptibility to Fake News About It) (2020, methodology): “We also replicated previous research showing that CRT performance predicts news discernment (e.g., Pennycook & Rand, 2019b, 2020).”
Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention (2020, background): “…2017; Van Bavel & Pereira, 2018), our data are more consistent with recent studies on political misinformation that provide both correlational (Pennycook & Rand, 2019b; including data from Twitter sharing, Mosleh, Pennycook, Arechar, & Rand, 2020) and experimental (Bago, Rand, & Pennycook,…”
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online (2019, background): “For instance, shifting people’s attention to other aspects of fake news content such as the trustworthiness of the source may also help to combat the consumption and spread of fake news (Pennycook & Rand, 2019).”
A digital media literacy intervention increases discernment between mainstream and false news in the United States and India (2020, methodology): “We selected these stories from hyperpartisan sources identified in prior work (58) (SI Appendix, section H).”
Emphasizing publishers does not effectively reduce susceptibility to misinformation on social media (2020, methodology): “Yet, others (Austin & Dong, 1994, Pennycook & Rand, 2019b, Jakesch et al., 2018) have failed to demonstrate that assessments of news stories are changed by source information.”
Partisan Polarization Is the Primary Psychological Motivation behind Political Fake News Sharing on Twitter (2020, background): “Better performance on the CRT correlates positively with the ability to discern political fake news from real news (Pennycook and Rand 2019b).”
Identity concerns drive belief: The impact of partisan identity on the belief and dissemination of true and false news (2018, background): “…over 2 billion Facebook users were subject to a news rating system that prioritizes trusted news sources in their feed (Dwoskin & Shaban, 2018; Pennycook & Rand, 2019), and scientists across numerous disciplines are also striving to tackle this issue (Lazer et al., 2018; Roozenbeek & van der…”
The Implied Truth Effect: Attaching Warnings to a Subset of Fake News Headlines Increases Perceived Accuracy of Headlines Without Warnings (2020, background): “…that individuals who are more analytic and deliberative are less likely to believe politically concordant false news (Bronstein et al. 2019; Pennycook and Rand 2019b, c), rather than more likely according to the motivated cognition account (Kahan et al. 2012, Kahan 2017), and that insofar…”
Moderating with the Mob: Evaluating the Efficacy of Real-Time Crowdsourced Fact-Checking (2021, background): “Previous studies suggest that although ordinary people can distinguish between lower-and higher-quality news sources (Pennycook and Rand 2019a), they have high error rates when assessing the veracity of false news headlines and full articles (Aslett et al. 2021; Pennycook and Rand 2019b).”
Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis (2020, background): “Specifically, interventions have employed a variety of strategies designed to debunk misinformation, including algorithmic corrections (Bode & Vraga, 2015) and user-generated credibility ratings (Pennycook & Rand, 2019).”
Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective (2020, background): “First, the quantity and quality of available information have a significant impact on developing SMF (Bright et al., 2015; Pentina and Tarafdar, 2014).”
The Reception of Fake News: The Interpretations and Practices That Shape the Consumption of Perceived Misinformation (2019, background): “It has largely resorted to cognitive and social networks frameworks, and from computational methods, which have been able to illustrate the spread of falsehood online (Allcott and Gentzkow 2017; Guess, Nagler, and Tucker 2019; Pennycook and Rand, 2019a; Tandoc Jr, Wei Lim, and Richard Ling 2018).”
Fake news, disinformation and misinformation in social media: a review (2023, background): “Unlike the previously mentioned researchers who focus on news content in their approaches, Pennycook and Rand (2019) focus on using crowdsourced judgments of the quality of news sources to combat social media disinformation.”
The Psychology of Fake News. (2021, neutral): “Despite potential concerns about political bias or lack of knowledge, recent work has found high agreement between layperson crowds and fact-checkers when evaluating the trustworthiness of news publishers: the average Democrat, Republican, and fact-checker all gave fake news and hyperpartisan sites ”
The future of social media in marketing (2019, background): “In response, research has begun to explore ways that social media platforms can combat fake news through algorithms that determine the quality of shared content (e.g., Pennycook and Rand 2019).”
The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation (2023, background): “Moreover, the “partisan content” that ChatGPT automatically generates at unprecedented scales may attract users who share similar beliefs (44).”