2022
By Marthe Möller, Susan Vermeer, and Susanne Baumgartner
The past years have seen a rise in studies investigating how user comments influence the experiences of social media users. The various studies that have been conducted on this topic so far all seem to be based on the same assumption, namely that comments are relevant in the sense that they actually discuss the main content that they accompany (e.g., a YouTube video, Instagram image). The present project tests this assumption by creating a tool that can detect the relevance of comments (i.e., whether or not a comment discusses the main content that it accompanies). Using comments written in response to music videos posted on YouTube, we train a machine that can automatically classify comments. Scholars can use this machine to verify the assumption that the comments that they study are relevant (and hence, can influence how viewers experience social media content).
This project is reported in the following publication: Möller, A. M., Vermeer, S. A. M., & Baumgartner, S. E. (2024). Cutting through the comment chaos: A Supervised Machine Learning approach to identifying relevant YouTube comments. Social Science Computer Review 42(1), 162-185. https://doi.org/10.1177/08944393231173895