Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach
<|reference_start|>Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach: In recent years, the United States has witnessed a significant surge in the popularity of vaping or e-cigarette use, leading to a notable rise in cases of e-cigarette and vaping use-associated lung injury (EVALI) that caused hospitalizations and fatalities during the EVALI outbreak in 2019, highlighting the urgency to comprehend vaping behaviors and develop effective strategies for cessation. Due to the ubiquity of social media platforms, over 4.7 billion users worldwide use them for connectivity, communications, news, and entertainment with a significant portion of the discourse related to health, thereby establishing social media data as an invaluable organic data resource for public health research. In this study, we extracted a sample dataset from one vaping sub-community on Reddit to analyze users' quit-vaping intentions. Leveraging OpenAI's latest large language model GPT-4 for sentence-level quit vaping intention detection, this study compares the outcomes of this model against layman and clinical expert annotations. Using different prompting strategies such as zero-shot, one-shot, few-shot and chain-of-thought prompting, we developed 8 prompts with varying levels of detail to explain the task to GPT-4 and also evaluated the performance of the strategies against each other. These preliminary findings emphasize the potential of GPT-4 in social media data analysis, especially in identifying users' subtle intentions that may elude human detection.<|reference_end|>
@article{vuruma2024can,
    title={Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of
  Automatic Data Annotation Approach},
    author={Sai Krishna Revanth Vuruma, Dezhi Wu, Saborny Sen Gupta, Lucas Aust,
  Valerie Lookingbill, Wyatt Bellamy, Yang Ren, Erin Kasson, Li-Shiun Chen,
  Patricia Cavazos-Rehg, Dian Hu and Ming Huang},
    journal={arXiv preprint arXiv:2407.00167},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00167},
    primaryClass={cs.CL cs.AI cs.ET cs.HC cs.SI}
}