“Algorithm-driven recommendation systems amplify extreme viewpoints more than moderate ones.”

“Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studies using real user traces suggest that recommender systems are not the primary driver of attention toward extreme content; on the contrary, such content is mostly reached through other means... although blindly following recommendations would indeed lead users to niche content, users rarely consume niche content when given the option because it is of low utility to them, which can lead the recommender system to deamplify such content.”

“We present evidence that challenges this dominant view, drawing on three large-scale, multi-wave experiments with a combined N of 7,851 human users, consistently showing that extremizing algorithmic recommendations has limited effects on opinions. We use data on over 125,000 experimentally manipulated recommendations and 26,000 platform interactions to estimate how recommendation algorithms alter users’ media consumption decisions and, indirectly, their political attitudes. Our work builds on recent observational studies showing that algorithm-driven “rabbit holes” of recommendations may be less prevalent than previously thought.”

“Rabbit holes capture the process by which a user starts in a rich information environment and ends up in an ideologically extreme echo chamber. Recommendation systems provide a self-reinforcing feedback loop whereby users click on content that they like, and YouTube provides a more intense version of that content.”

“We find that one platform—YouTube—does amplify extreme and fringe content, while two—Reddit and Gab—do not... after applying an extreme treatment, far-right content was ranked higher on average than moderate.”

“All three studies' findings suggest that extremist content may be amplified via YouTube's recommendation algorithm. Conversely, Ledwich and Zaitsev (2019) find that YouTube recommendation algorithms actively discourage users from visiting extreme content online... We find that one platform—YouTube—does amplify extreme and fringe content, while two—Reddit and Gab—do not.”

“Recommendation systems can promote extreme content, but this largely plays a smaller role than individuals' own choices. ... In the literature review mentioned above, 10 of the 15 studies demonstrated some kind of positive effect. ... research paints a complex picture in which there are many moving variables. There is certainly insufficient evidence to support claims of ‘radicalisation by algorithm’; they are one small piece of a puzzle.”

“At the same time, the issue of filter bubbles may also be intensified under the precise modeling of user interests via LLMs. Users are confined to a narrow range of interests by the RS, which diminishes the diversity of information and reinforces users' existing viewpoints and preferences, making it difficult for them to access diverse perspectives.”

“This fundamentally limits our ability to test for algorithmic amplification... any “algorithmic amplification” that would be detected by comparing the exposure the preferred accounts received to the exposure those accounts would have gotten under reverse chron would only account for the marginal amplification due to the ranking model alone.”

““On average, the recommended videos on the sidebar shifted toward moderate content after about 30 videos,” Hosseinmardi says, “while homepage ...””

“This Insight highlights that algorithmic amplification of TVEC is influenced by user agency alongside algorithm design, as users typically get what they seek.”

“The report found that Meta's algorithm proactively amplified and promoted content that incited violence, hatred and discrimination.”

“The algorithms driving social media are called recommender systems. These algorithms are the engine that makes Facebook and YouTube what they are.”