Abstract

Fact-checking databases, as important results of fact checkers’ epistemic work, are increasingly tied together in new overarching infrastructures, but these are understudied and lack transparency despite being an important societal baseline for whether claims are false. This article conceptualizes fact-checking as infrastructure and constructs a mixed-methods approach to examine overlaps and differences and thereby detect biases to increase transparency in COVID-19 misinformation infrastructure at scale. Analyzing Poynter and Google as such overarching infrastructures, we found only a small overlap. Fewer fact-checkers contribute to Google, with fewer stories than to Poynter. 75% of claims in Google are fact-checked by Asian and North American fact-checkers (44% for Poynter) but none by South Americans (20% for Poynter). More stories in Poynter originate from Facebook than outside social media (43% vs. 17%), while Google shows the opposite (16% vs. 38%). In Google, claims originate to a larger extent from public persons. We find similar large topics on “statistics” and “cures,” but also differences regarding smaller topics (e.g., “vaccines”) and types of misinformation (e.g., “virus characteristics”). Thus, the article shows that the infrastructures have inherent biases and argue that making visible such biases will increase transparency for stakeholders using it.

摘要

事实核查数据库作为事实核查人员认知工作的重要成果，在新的总体基础设施中越来越多地联系在一起，但尽管这些是判断声明是否虚假的重要社会基线，但这些数据库却没有得到充分研究并且缺乏透明度。本文将事实核查概念化为基础设施，并构建了一种混合方法来检查重叠和差异，从而检测偏差，以大规模提高 COVID-19 错误信息基础设施的透明度。分析 Poynter 和 Google 作为这样的总体基础设施，我们发现只有很小的重叠。与波因特相比，为谷歌做出贡献的事实核查人员更少，故事也更少。Google 中 75% 的声明都经过亚洲和北美事实核查人员的事实核查（Poynter 为 44%），但南美人则没有（Poynter 为 20%）。Poynter 中来自 Facebook 的故事多于来自社交媒体之外的故事（43% 对 17%），而谷歌则相反（16% 对 38%）。在谷歌，索赔在很大程度上来自公众。我们在“统计”和“治疗”方面发现了类似的大主题，但在较小主题（例如“疫苗”）和错误信息类型（例如“病毒特征”）方面也存在差异。因此，文章表明基础设施具有固有的偏见，并认为使这种偏见可见将增加使用它的利益相关者的透明度。“疫苗”）和错误信息的类型（例如，“病毒特征”）。因此，文章表明基础设施具有固有的偏见，并认为使这种偏见可见将增加使用它的利益相关者的透明度。“疫苗”）和错误信息的类型（例如，“病毒特征”）。因此，文章表明基础设施具有固有的偏见，并认为使这种偏见可见将增加使用它的利益相关者的透明度。