In response to intense pressure, technology companies have enacted policies to combat misinformation^1,2,3,4. The enforcement of these policies has, however, led to technology companies being regularly accused of political bias^5,6,7. We argue that differential sharing of misinformation by people identifying with different political groups^{8,9,10,11,12,13,14,15} 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.

为了应对巨大压力，科技公司制定了打击错误信息的政策^1,2,3,4。然而，这些政策的实施导致科技公司经常被指责存在政治偏见^5,6,7。我们认为，认同不同政治群体的人对错误信息的差异分享^{8,9,10,11,12,13,14,15} 可能会导致政治执法不对称，即使是公正的政策。我们首先分析了 2020 年美国总统大选期间 9,000 名政治活跃的 Twitter 用户。尽管估计为支持特朗普/保守派的用户确实比估计为支持拜登/自由派的用户更有可能被暂停，但支持特朗普/保守派的用户也分享了更多指向各种低质量新闻网站的链接——即使新闻质量是由政治上平衡的外行群体决定的，或者只有共和党外行群体决定的——并且估计成为机器人的可能性更高。我们在 2016 年至 2023 年的其他 7 个 Twitter、Facebook 和调查实验的分享数据集中发现，陈述或推断的保守主义与低质量新闻分享（基于专家和政治平衡的外行评级）之间存在类似的关联，包括来自 16 个不同国家的数据。因此，即使在政治中立的反错误信息政策下，执法方面的政治不对称也应该是可以预料的。执法中的政治不平衡并不一定意味着社交媒体公司实施反错误信息政策存在偏见。