![]() ![]() ![]() Recent work by journalists, civil society, and scholars have provided important new evidence of the role of misinformation in the recent election, building on the substantial body of work around the impact of misinformation on electoral processes (Grinberg et al., 2019 Persily & Tucker, 2020 Tucker et al., 2018). These messages were produced by various speakers, covered a wide range of topics, and were distributed within and across different media ecosystems (CIP et al., 2021). elections suggests that misinformation related to the presidential campaign once again circulated widely on and offline (Scott & Overly, 2020). Much like four years ago, evidence throughout the 2020 U.S. This study emphasizes the importance of researching content moderation at the ecosystem level, adding new evidence to a growing public and platform policy debate around implementing effective interventions to counteract misinformation. ![]() Our findings underscore the networked nature of misinformation: posts or messages banned on one platform may grow on other mainstream platforms in the form of links, quotes, or screenshots.It nonetheless provides valuable descriptive evidence of the broad cross-platform diffusion of messages that Twitter had flagged as containing election-related misinformation. These observational data do not enable us to determine whether this finding is a selection effect (i.e., Twitter intervened on posts that were more likely to spread) or causal (Twitter’s intervention increased their spread).We find that messages that had been blocked from engagement on Twitter were posted more often and received more visibility on other popular platforms than messages that were labeled by Twitter or that received no intervention at all.To understand the impact of one platform’s intervention on their broader spread, we identify these same messages on Facebook, Instagram, and Reddit and collect data from those platforms.We find that while blocking messages from engagement effectively limited their spread, messages that were flagged by the platform with a warning label spread further and longer than unlabeled tweets.We then collect data from Twitter in order to measure the differential spread of messages that were not flagged and those that were flagged by a warning label or prevented from being engaged with. We identify tweets from Former President Donald Trump, posted from Novemthrough January 8, 2021, that were flagged by Twitter as containing election-related misinformation.How did messages flagged by Twitter spread on Facebook, Instagram, and Reddit?.How did messages flagged by Twitter spread on the platform compared to messages without interventions?.The Trump administration has so far not cooperated with Biden’s transition team, which officials warn could undermine the COVID-19 pandemic response and national security in the early days of a Biden administration.Ī growing chorus of bipartisan observers have called for the process to begin in earnest. He has alleged, without evidence, that widespread fraud and voting irregularities took place.ĭubious claims, regularly made via Trump’s social media accounts, often earn the president cautionary labels from Twitter. While Biden was declared the winner of the US presidential election and is currently projected to win 306 Electoral College votes, far above the 270 votes needed for victory, Trump has refused to concede. The account, as well as the, and official accounts will be archived and reset to zero tweets for the incoming administration, according to Politico. While Trump more regularly uses his personal account, which has 88.9 million followers, he has used the official presidential account, which has 32.8 million followers, to amplify those messages throughout his presidency. ![]()
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