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Algorithmic extremism: Examining YouTube's rabbit hole of radicalization

机译：算法极端主义：研究YouTube激进的兔子洞

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The role that YouTube and its behind-the-scenes recommendation algorithm plays in encouraging online radicalization has been suggested by both journalists and academics alike. This study directly quantifies these claims by examining the role that YouTube’s algorithm plays in suggesting radicalized content. After categorizing nearly 800 political channels, we were able to differentiate between political schemas in order to analyze the algorithm traffic flows out and between each group. After conducting a detailed analysis of recommendations received by each channel type, we refute the popular radicalization claims. On the contrary, these data suggest that YouTube’s recommendation algorithm actively discourages viewers from visiting radicalizing or extremist content. Instead, the algorithm is shown to favor mainstream media and cable news content over independent YouTube channels with a slant towards left-leaning or politically neutral channels. Our study thus suggests that YouTube’s recommendation algorithm fails to promote inflammatory or radicalized content, as previously claimed by several outlets.
机译：记者和学者都暗示了YouTube及其幕后推荐算法在鼓励在线激进化方面的作用。这项研究通过研究YouTube算法在建议激进内容方面的作用，直接量化了这些说法。在对将近800个政治渠道进行分类之后，我们能够区分政治模式，以便分析算法流量的流出以及每个群体之间的流量。在对每种渠道类型收到的建议进行详细分析之后，我们驳斥了流行的激进主张。相反，这些数据表明，YouTube的推荐算法会积极阻止观看者访问激进或极端主义的内容。取而代之的是，该算法显示出比主流YouTube频道更偏爱主流媒体和有线新闻内容，并且偏向左倾或政治中立的频道。因此，我们的研究表明，YouTube的推荐算法无法促进煽动性或激进性的内容，正如之前多家媒体所声称的那样。

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