DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model

Stance detection of social media text is a key component of many real-world applications like evaluating marketing campaigns, evaluating political policies or candidates, or evaluating information environments. However, creating automatic stance labeling systems requires the manual annotation of stances, which is both tedious and resource-intensive. This paper introduces a stance labeling method that makes use of weak signals of sentence tone, then consolidating these signals with a Data Programmingmodel for the final stance label. In a time of international conflict, understanding the public opinion towards the country's military is crucial for recruitment. We present DIVERSE, a dataset involve stances towards YouTube videos of the US military (Dataset available at https://doi.org/10.5281/zenodo.10493803). On average, the videos have 200 comments each, and the stances skew slightly towards the "against" characterization for both the US army and the video.