Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.

人工智能 (AI) 支持的推荐系统在确定用户在社交媒体平台上接触的内容方面发挥着至关重要的作用。然而，这些系统的行为模式通常是不透明的，这使得评估其对虚假信息和错误信息的传播和消费的影响变得复杂。为了开始解决这一证据差距，本研究提出了一种测量方法，使用观察到的数字痕迹来推断 2023 年 1 月 14 天内 Twitter 上低可信度内容的算法放大状态。使用约 270 万条原始数据集针对平台上发布的有关 COVID-19 和气候变化的帖子，本研究识别了来自低可信域的分享信息的推文，并使用具有两个分层（即推文的参与度和用户的关注者级别）的引导模型来比较印象中的任何差异在低可信度和高可信度样本之间生成。还检查了毒性、政治偏见和验证状态等其他分层变量。该分析为 Twitter 算法是否有利于低可信度内容的可见性提供了宝贵的观察证据，结果表明，总体而言，包含低可信度 URL 域的推文比不跨两个数据集的推文表现更好。然而，这种影响很大程度上归因于高参与度、高关注者推文的差异，这些推文在印象生成方面非常有影响力，并且在包含低可信度内容时更有可能获得放大的可见性。此外，高毒性推文和具有右倾偏见的推文会被放大，来自经过验证的帐户的低可信度推文也是如此。最终，这表明 Twitter 的推荐系统可能通过放大低可信度内容的可见性以及由非常有影响力的用户产生的高参与度来促进虚假内容的传播。