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

arXiv: https://arxiv.org/abs/2403.03334 · HTML: https://mlsanity.com/paper/2403.03334/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [Can we trust the evaluation on ChatGPT?](https://mlsanity.com/paper/2303.12767/) (2023)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Language Models in the Loop: Incorporating Prompting into Weak Supervision](https://mlsanity.com/paper/2205.02318/) (2022)
- [Deep Learning Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2004.06465/) (2020)
- [Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media](https://mlsanity.com/paper/2304.03087/) (2023)
- [Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations](https://mlsanity.com/paper/2006.00691/) (2020)
- [Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media](https://mlsanity.com/paper/2305.13047/) (2023)
- [YouNICon: YouTube's CommuNIty of Conspiracy Videos](https://mlsanity.com/paper/2304.05274/) (2023)
- [Chain-of-Thought Embeddings for Stance Detection on Social Media](https://mlsanity.com/paper/2310.19750/) (2023)
- [It Takes Two to Negotiate: Modeling Social Exchange in Online Multiplayer Games](https://mlsanity.com/paper/2311.08666/) (2023)
- [A Logically Consistent Chain-of-Thought Approach for Stance Detection](https://mlsanity.com/paper/2312.16054/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media](https://mlsanity.com/paper/2410.08900/) (2024)
- [TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations](https://mlsanity.com/paper/2607.15240/) (2026)

## Abstract (arXiv)

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.