THE ALGORITHM PLAYGROUND: A CONTENT ANALYSIS OF USER-PRODUCED CHILDREN’S VIDEOS ON YOUTUBE

Abstract

With early-childhood mobile media device use on the rise, online video content plays an ever-increasing role in children’s lives. Of the wide variety of content available to children, user-produced videos on YouTube seem to be most popular. However, due to the platform’s size and the overwhelming number of child-targeted videos found on YouTube, scholars have been struggling with how to approach and study this topic. This study aims to address the gap in research by analyzing prevalent user-produced children’s videos on YouTube, with research questions focusing on video genres, their features, and content themes. Drawing on YouTube’s popularity-measurements and video recommendation algorithm, a corpus of 100 user-produced videos targeted to children was assembled. A content analysis of these videos led to the identification and conceptualization of 13 distinct genres of user-produced children’s videos: unboxing, surprise eggs, finger family, play-doh, nursery rhymes, kids songs, learning, pretend play (enactment), pretend play (toys), storytelling, arts & crafts, entertainer in character, and process repetition. Furthermore, the findings indicate that there are often unique interplays between genre type and the content, the production format, and the overall quality and educational rating. In addition to shedding light on the importance of studying child-targeted content on YouTube, this study’s main contribution is a typological map of the user-produced children’s video ecosystem that future studies from various fields can draw on.