DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model
This addresses the lack of data for assessing public perceptions of online military recruitment, which is crucial for recruitment strategies, but it is incremental as it applies existing weak supervision methods to a new domain-specific dataset.
The paper tackles the problem of analyzing public opinion on military recruitment in digital spaces by introducing the DIVERSE dataset of YouTube comments from the U.S. Army's channel, using a weak supervision approach with large language models for stance labeling, and finds a balanced stance distribution with a slight skew towards oppositional comments post-2021.
Public opinion of military organizations significantly influences their ability to recruit talented individuals. As recruitment efforts increasingly extend into digital spaces like social media, it becomes essential to assess the stance of social media users toward online military content. However, there is a notable lack of data for analyzing opinions on military recruiting efforts online, compounded by challenges in stance labeling, which is crucial for understanding public perceptions. Despite the importance of stance analysis for successful online military recruitment, creating human-annotated, in-domain stance labels is resource-intensive. In this paper, we address both the challenges of stance labeling and the scarcity of data on public opinions of online military recruitment by introducing and releasing the DIVERSE dataset: https://doi.org/10.5281/zenodo.10493803. This dataset comprises all comments from the U.S. Army's official YouTube Channel videos. We employed a state-of-the-art weak supervision approach, leveraging large language models to label the stance of each comment toward its respective video and the U.S. Army. Our findings indicate that the U.S. Army's videos began attracting a significant number of comments post-2021, with the stance distribution generally balanced among supportive, oppositional, and neutral comments, with a slight skew towards oppositional versus supportive comments.