Exploring User Responses to Entertainment and Political Videos: An Automated Content Analysis of YouTube

Abstract

On YouTube, videos are always presented together with additional user-generated information about those videos. This social information is presented in the form of number of views, (dis)likes, or comments. However, we know little about the characteristics of social information about entertainment videos. To fill this gap, the present study examined the amount and valence of online entertainment videos' social information and compared this to the social information of online political videos. An automated content analysis of (dis)likes, views, and 39,602 comments presented alongside 463 videos showed that entertainment videos received more views and comments than political videos. Moreover, entertainment videos' comments were more neutral than political videos' comments. We also found that comments with a stronger positive or negative valence received more replies and likes, with the exception that the positive valence of political videos had no effect. Finally, we found that as political videos received more comments, the positive valence of their comments became more consistent. Overall, these results show that the type of video influences the amount and valence of social information the video receives.

Commenting and connecting: A thematic analysis of responses to YouTube vlogs about borderline personality disorder

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“…In psychology, YouTube is a useful research tool ( Chou et al, 2011 ; Khasawneh et al, 2020 ; Konijn et al, 2013 ; Miller, 2015 ; Möller et al, 2018 ). YouTube can be used to share experiences, to seek mental health information and advice, and to share first-hand experiences of mental illness ( Mertan et al, 2021 ; Naslund et al, 2016 ; Woloshyn and Savage, 2020 ; Yoo and Kim, 2012 ).…”

Comparing Platform “Ranking Cultures” Across Languages: The Case of Islam on YouTube in Scandinavia

“…He found that “the top 3% most viewed channels account for 28% of all uploads and 85% of all views” (p. 26). Möller, Kühne, Baumgartner, and Peter (2018) performed an analysis of traces left by users (view numbers, [dis]likes, and comments) using API data, comparing entertainment and political videos. Airoldi, Beraldo, and Gandini (2016) “followed” the YouTube recommender algorithm to study networks of music videos.…”

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