DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909369665323008 |
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| author | Cruickshank, Iain J. Soofi, Amir Ng, Lynnette Hui Xian |
| author_facet | Cruickshank, Iain J. Soofi, Amir Ng, Lynnette Hui Xian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03334 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model Cruickshank, Iain J. Soofi, Amir Ng, Lynnette Hui Xian Computation and Language Artificial Intelligence 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. |
| title | DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.03334 |