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while the data refute all the other three claims. Rejection of these claims seems to be in line with studies that critique the claims of YouTube’s algorithm as a pathway to radicalization [50]. TABLE II CLAIMS AND DATA SUPPORT Claim C1 - Radical Bubbles. Recommendations influence viewers of radical content to watch more similar content than they would otherwise, making it less likely that alternative views are presented. C2 - Right-Wing Advantage. YouTube’s recom- mendation algorithm prefers right-wing content over other perspectives. C3 - Radicalization Influence. YouTube’s algorithm influences users by exposing them to more extreme content than they would otherwise. Data Support Partially supported Not supported Not supported C4 - Right-Wing Radicalization Pathway. YouTube algorithm influences viewers of mainstream and center-left channels by recommending extreme right-wing content, content that aims to disparage left-wing or centrist narratives. Not supported Fig. 10. Algorithmic advantage for Fox News independent YouTubers into another group and comparing the algorithmic advantages and disadvantages for each. The third group we separated from mainstream media and YouTubers is the group we called the ”Missing Link Media.” This group encompasses media outlets that have financial backing with the traditional mainstream outlets but are not considered part of the conventional mainstream media. For example, left-wing channels such as Vox or Vice belong to this category, while BlazeTV is an equivalent for the right-leaning media. Figure 11 shows the clear advantage mainstream media channels receive over both independent channels and Missing Link Media channels. Fig. 11. Algorithmic Advantage of Mainstream Media Finally, based on the findings and analysis of our four claims, we conclude that these data offer little support to the claims that YouTube’s recommendation algorithm will recom- mend content that might be contributing to the radicalization of the user-base. Only the first claim is partially supported, YouTube has stated that its algorithm will favor more recent videos that are popular both in terms of views as well as engagement [38]. The algorithm will recommend more videos based on a user profile, or the most current, popular videos for anonymous viewers. YouTube has stated that they are attempting to maximize the likelihood that a user will enjoy their recommended videos and will remain on the platform for as long as possible. The viewing history determines whether the algorithm will recommend the viewer more extreme content. Antithetical to this claim is that our data show that even if the user is watching very extreme content, their recommendations will be populated with a mixture of extreme and more mainstream content. YouTube is, therefore, more likely to steer people away from extremist content rather than vice versa. V. LIMITATIONS AND CONCLUSIONS There are several limitations to our study that must be considered for the future. First, the main limitation is the anonymity of the data set and the recommendations. The recommendations the algorithm provided were not based on videos watched over extensive periods. We expect and have anecdotally observed that the recommendation algorithm gets more fine-tuned and context-specific after each video that is watched. However, we currently do not have a way of collecting such information from individual user accounts, but our study shows that the anonymous user is generally directed towards more mainstream content than extreme. Sim- ilarly, anecdotal evidence from a personal account shows that YouTube suggests content that is very similar to previ- ously watched videos while also directing traffic into more mainstream channels. That is, contrary to prior claims; the algorithm does not appear to stray into suggesting videos several degrees away from a user’s normal viewing habits.PDF Image | Algorithmic Extremism: Examining YouTube’s Rabbit Hole of Radicalization
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