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http://dx.doi.org/10.5392/JKCA.2021.21.05.001

An Analysis of Filter Bubble Phenomenon on YouTube Recommendation Algorithm Using Text Mining  

Shin, Yoo Jin (LGU+)
Lee, Sang Woo (연세대 정보대학원)
Publication Information
The Journal of the Korea Contents Association / v.21, no.5, 2021 , pp. 1-10 More about this Journal
Abstract
This study empirically confirmed 'the political bias of the YouTube recommendation algorithm' and 'the selective exposure of user' to verify the Filter Bubble phenomenon of YouTube. For the experiment, two new YouTube accounts were opened and each account was trained simultaneously in a conservative and a liberal account for a week, and the "Recommended" videos were collected from each account every two days. Subsequently, through the text mining method, the goal of the research was to investigate whether conservative videos are more recommended in a righties account or lefties videos are more recommended in a lefties account. And then, this study examined if users who consumed political news videos via YouTube showed "selective exposure" received selected information according to their political orientation through a survey. As a result of the Text Mining, conservative videos are more recommended in the righties account, and liberal videos are more recommended in the lefties account. Additionally, most of the videos recommended in the righties/lefties account dealt with politically biased topics, and the topics covered in each account showed markedly definitive differences. And about 77% of the respondents showed selective exposure.
Keywords
YouTube; Recommendation Algorithm; Filter Bubble; News; Political Bias; Selective Exposure;
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