Differences in misinformation sharing can lead to politically asymmetric sanctions

In response to intense pressure, technology companies have enacted policies to combat misinformation<sup>1-4</sup>. The enforcement of these policies has, however, led to technology companies being regularly accused of political bias<sup>5-7</sup>. We argue that differential sharing of misinformation by people identifying with different political groups<sup>8-15</sup> could lead to political asymmetries in enforcement, even by unbiased policies. We first analysed 9,000 politically active Twitter users during the US 2020 presidential election. Although users estimated to be pro-Trump/conservative were indeed substantially more likely to be suspended than those estimated to be pro-Biden/liberal, users who were pro-Trump/conservative also shared far more links to various sets of low-quality news sites-even when news quality was determined by politically balanced groups of laypeople, or groups of only Republican laypeople-and had higher estimated likelihoods of being bots. We find similar associations between stated or inferred conservatism and low-quality news sharing (on the basis of both expert and politically balanced layperson ratings) in 7 other datasets of sharing from Twitter, Facebook and survey experiments, spanning 2016 to 2023 and including data from 16 different countries. Thus, even under politically neutral anti-misinformation policies, political asymmetries in enforcement should be expected. Political imbalance in enforcement need not imply bias on the part of social media companies implementing anti-misinformation policies.

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Publication Details

Journal
Nature
Published
2024-10-02
DOI
https://doi.org/10.1038/s41586-024-07942-8
Citations
29
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
32.83
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article

Differences in misinformation sharing can lead to politically asymmetric sanctions

Mohsen Mosleh, David G. Rand, Qi Yang, Tauhid Zaman et al.
29 citations
Nature
Misinformation and Its Impacts
32.83
article

Differences in misinformation sharing can lead to politically asymmetric sanctions

Mohsen Mosleh, David G. Rand, Qi Yang, Tauhid Zaman, Gordon Pennycook
article en
29 citations

Abstract

In response to intense pressure, technology companies have enacted policies to combat misinformation<sup>1-4</sup>. The enforcement of these policies has, however, led to technology companies being regularly accused of political bias<sup>5-7</sup>. We argue that differential sharing of misinformation by people identifying with different political groups<sup>8-15</sup> could lead to political asymmetries in enforcement, even by unbiased policies. We first analysed 9,000 politically active Twitter users during the US 2020 presidential election. Although users estimated to be pro-Trump/conservative were indeed substantially more likely to be suspended than those estimated to be pro-Biden/liberal, users who were pro-Trump/conservative also shared far more links to various sets of low-quality news sites-even when news quality was determined by politically balanced groups of laypeople, or groups of only Republican laypeople-and had higher estimated likelihoods of being bots. We find similar associations between stated or inferred conservatism and low-quality news sharing (on the basis of both expert and politically balanced layperson ratings) in 7 other datasets of sharing from Twitter, Facebook and survey experiments, spanning 2016 to 2023 and including data from 16 different countries. Thus, even under politically neutral anti-misinformation policies, political asymmetries in enforcement should be expected. Political imbalance in enforcement need not imply bias on the part of social media companies implementing anti-misinformation policies.

NatureVol. 634(8034)
McGovern Institute for Brain Research (US), Cornell University (US), University of Exeter (GB), Yale University (US), University of Oxford (GB), Institute of Cognitive and Brain Sciences (US), Massachusetts Institute of Technology (US)
Reduced inequalities
Openalex Percentile: Top 0%
Misinformation and Its Impacts
32.83
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