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the growth belonging to the centrist IDW category reflected a deradicalization trend rather than further radicalization. Never- theless, these authors are critical towards claims that watching content on Youtube will lead to the spread of radical ideas like a ”zombie bite” and are further critical of the potential pipeline from moderate, centrist channels to radical right-wing content. III. ANALYZING THE YOUTUBE RECOMMENDATION ALGORITHM Our study focuses on the YouTube recommendation algo- rithm and the direction of recommendations between different groups of political content. To analyze the common claims from media and other researchers, we have distilled them into specific claims that can be assessed using our data set. 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 recommendation 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 seek out. 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. By analyzing whether the data supports these claims, we will be able to draw preliminary conclusions on the impact of the recommendation algorithm. A. YouTube Channel Selection Criteria The data for this study is collected from two sources. First, YouTube offers a few tools for software developers and researchers. Our research applies an application programming interface (API) that YouTube provides for other websites that integrate with YouTube and also for research purposes to de- fine the channel information, including view and engagement statistics and countries. However, the YouTube API limited the amount of information we could retrieve and the period it could be kept and was thus not entirely suitable for this study. For this reason, we use an additional scraping algorithm that provides us information on individual video statistics such as views, likes, video title, and closed captions. This algorithm offers data since the first of January, 2018. The scraping algorithm also provides us the primary data applied for this study: the recommendations that YouTube’s recommendation algorithm offers for each video. The scraping process runs daily. The scraped data, as well as the YouTube API, provides us a view of the recommendations presented to an anonymous account. In other words, the account has not ”watched” any videos, retaining the neutral baseline recommendations, de- scribed in further detail by YouTube in their recent paper that explains the inner workings of the recommendation algorithm [38]. One should note that the recommendations list provided to a user who has an account and who is logged into YouTube might differ from the list presented to this anonymous account. However, we do not believe that there is a drastic difference in the behavior of the algorithm. Our confidence in the similarity is due to the description of the algorithm provided by the developers of the YouTube algorithm [38]. It would seem counter-intuitive for YouTube to apply vastly different criteria for anonymous users and users who are logged into their accounts, especially considering how complex creating such a recommendation algorithm is in the first place. The study includes eight hundred and sixteen (816) channels which fulfill the following criteria: • Channel has over ten thousand subscribers. • More than 30 percent of the content on the channel is political. The primary channel selection was made based on the number of subscriptions. The YouTube API provides channel details, including the number of subscribers and aggregate views of all time on the channel. The sizes of the bubble are based on the video views in the year 2018m, not the subscriber counts. YouTube also provides detailed info on the views of each video and dislikes, thus providing information on the additional engagement each video receives from the users. Generally, only channels that had over ten thousand sub- scriptions were analyzed. However, if the channel’s subscrip- tion numbers were lower than our threshold value or there were missing data. However, if the channel is averaging over ten thousand views per month, the channel was still included. We based our selection criteria on the assumption that tiny channels with minimal number of views or subscriptions are unlikely to fulfill YouTube’s recommendation criteria: ”1) engagement objectives, such as user clicks, and degree of en- gagement with recommended videos; 2) satisfaction objectives, such as user liking a video on YouTube, and leaving a rating on the recommendation [38].” Another threshold for the channels was the focus of the content: only channels where more than 30 percent of the content was on US political or cultural news or cultural commentary, were selected. We based the cultural commentary selection on a list of social issues on the website ISideWith. A variety of qualitative techniques compiled the list of these channels. The lists provided by Ad Fontes Media provides a starting point for the more mainstream and well-known alternative sites. Several blogs and other websites further list political channels or provide tools for advanced searches based on topics [39] [40] [41]. We also analyzed the recent academic studies and their lists of channels such as Ribero et al. (2019) and Munger and Philips (2019). However, not all channels included in these two studies fit our selection criteria. Thus one can observe differences between the channel lists and categories between our research and other recent studies on a similar subject.PDF Image | Algorithmic Extremism: Examining YouTube’s Rabbit Hole of Radicalization
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