Replication Data for: Distorsions of Political Bias in Crowdsourced Misinformation Flagging

Many people consume news on social media, yet the production of news items online has come under crossfire due to the common spreading of misinformation. Social media platforms police their content in various ways. Primarily they rely on crowdsourced “flags”: users signal to the platform that a specific news item might be misleading and, if they raise enough of them, the item will be fact-checked. However, real-world data show that the most flagged news sources are also the most popular and – supposedly – reliable ones. In this paper, we show this phenomenon can be explained by the unreasonable assumptions current content policing strategies make about how the online social media environment is shaped. The most realistic assumption is that confirmation bias will prevent a user from flagging a news item if they share the same political bias as the news source producing it. We show, via agent-based simulations, that a model reproducing our current understanding of the social media environment will necessarily result in the most neutral and accurate sources receiving most flags.