YouTube has revolutionized the way people discover and consume video. Although YouTube facilitates easy access to hundreds of well-produced and trustworthy videos, abhorrent, misinformative, and mistargeted content is also common. The platform is plagued by various types of problematic content: 1) disturbing videos targeting young children; 2) hateful and misogynistic content; and 3) pseudoscientific misinformation. While YouTube's recommendation algorithm plays a vital role in increasing user engagement and YouTube's monetization, its role in unwittingly promoting problematic content is not entirely understood. In this thesis, we shed some light on the degree of problematic content on YouTube and the role of the recommendation algorithm in the dissemination of such content. Following a data-driven quantitative approach, we analyze thousands of videos on YouTube, to shed light on: 1) the risks of YouTube media consumption by young children; 2) the role of the recommendation algorithm in the dissemination of misogynistic content, by focusing on the Involuntary Celibates (Incels) community; and 3) user exposure to pseudoscientific content on various parts of the platform and how this exposure changes based on the user's watch history. Our analysis reveals that young children are likely to encounter disturbing content when they randomly browse the platform. By analyzing the Incel community on YouTube, we find that Incel activity is increasing over time and that platforms may play an active role in steering users towards extreme content. Finally, when studying pseudoscientific misinformation, we find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth) than for emerging ones (like COVID-19) and that these recommendations are more common on the search results page than on a user's homepage or the video recommendations section.
翻译:虽然YouTube为获取成百上千个制作良好和值得信赖的视频提供了方便,但令人憎恶、信息失常和目标错误的内容也很常见。这个平台受到各类问题内容的困扰:1)针对幼儿的令人不安的视频;2)仇恨和厌恶的视频内容;3)假科学错误。尽管YouTube的建议算法在增加用户参与和YouTube的货币化方面发挥了至关重要的作用,但它在不知情地推广问题内容方面的作用并不完全被理解。在这个论文中,我们对YouTube上存在问题的内容的程度以及建议算法在传播这类内容方面的作用有些了解。根据数据驱动的数量方法,我们在YouTube上分析数千个视频,以说明:(1)YouTube媒体对幼儿的消费风险;2)YouTube的建议算法在传播错误的视频内容方面发挥着至关重要的作用,但该算法在非自愿的Celibibat(Incel)社群社群中发现,用户在阅读这些内容时会比普通网站看到更多的假科学内容的变化。我们的分析显示,在浏览历史时,年轻用户会发现这些小的平台可能会让儿童感到自己在不断阅读。