Election Fraud, YouTube, and Public Perception of the Legitimacy of President Biden

Abstract

Skepticism about the outcome of the 2020 presidential election in the United States led to a historic attack on the Capitol on January 6th, 2021 and represents one of the greatest challenges to America's democratic institutions in over a century. Narratives of fraud and conspiracy theories proliferated over the fall of 2020, finding fertile ground across online social networks, although little is know about the extent and drivers of this spread. In this article, we show that users who were more skeptical of the election's legitimacy were more likely to be recommended content that featured narratives about the legitimacy of the election. Our findings underscore the tension between an "effective" recommendation system that provides users with the content they want, and a dangerous mechanism by which misinformation, disinformation, and conspiracies can find their way to those most likely to believe them.

Algorithm-Mediated Social Learning in Online Social Networks

“…The Facebook algorithm decreases out-partisan news stories compared to an unsorted feed (Levy, 2021), and broader studies of multiple social media sites find amplification of in-partisan content as well (Nikolov et al, 2019). YouTube users who were most skeptical of the 2020 election results were three times more likely to be recommended videos that questioned the legitimacy of the 2020 election than users who did not express skepticism (Bisbee et al, 2022). Similarly, the longer that partisan users follow recommendations of the Youtube algorithm, the more their videos become biased toward their own partisan identity (Brown et al, 2022;Kaiser & Rauchfleisch, 2020).…”

“…In algorithm-mediated social learning environments, users may perceive these extreme narratives as more common and normative than they really are (Brady et al, in press). For example, Trump voters were much more likely to be presented with extremist views about fraud in the 2020 election by content algorithms (Bisbee et al, 2022). Seeing this promoted content, especially if it is associated with prestigious individuals (e.g., senators or House representatives), may encourage users to infer that beliefs about election fraud are widespread and held with little doubt, increasing the likelihood that users adopt these beliefs.…”

“…Algorithm-mediated social learning has far-reaching implications. Algorithms impact how we encounter moral and political issues on Twitter and Facebook (Chakradhar, 2021;Chowdhury, 2021;Huszár et al, 2022), how videos go viral on TikTok (Arthurs et al, 2018), and which conspiracy theories are promoted on YouTube (Bisbee et al, 2022;Schmitt et al, 2018). Algorithms filter how we learn about current events and even what we find out about our friends (Latzer et al, 2016).…”

Uncommon Yet Consequential Online Harms

“…always click on the second recommendation) from a randomly chosen seed video. They find that recommendations to videos featuring election-fraud related narratives were uncommon overall, and largely concentrated among a small number of individuals who self-reported high skepticism about the election's legitimacy (Bisbee et al 2022).…”

Subscriptions and external links help drive resentful users to alternative and extremist YouTube channels

“…We address this problem by constructing a specific set of rules to define a rabbit hole event. This definition builds on and reaffirms prior work (21,(24)(25)(26), and we applied it to our dataset to measure the prevalence of radicalization rabbit holes among U.S. YouTube users in 2020.…”