Utilizing Large Language Models to Identify Reddit Users Considering Vaping Cessation for Digital Interventions
<|reference_start|>Utilizing Large Language Models to Identify Reddit Users Considering Vaping Cessation for Digital Interventions: The widespread adoption of social media platforms globally not only enhances users' connectivity and communication but also emerges as a vital channel for the dissemination of health-related information, thereby establishing social media data as an invaluable organic data resource for public health research. The surge in popularity of vaping or e-cigarette use in the United States and other countries has caused an outbreak of e-cigarette and vaping use-associated lung injury (EVALI), leading to hospitalizations and fatalities in 2019, highlighting the urgency to comprehend vaping behaviors and develop effective strategies for cession. In this study, we extracted a sample dataset from one vaping sub-community on Reddit to analyze users' quit vaping intentions. Leveraging large language models including both the latest GPT-4 and traditional BERT-based language models for sentence-level quit-vaping intention prediction tasks, this study compares the outcomes of these models against human annotations. Notably, when compared to human evaluators, GPT-4 model demonstrates superior consistency in adhering to annotation guidelines and processes, showcasing advanced capabilities to detect nuanced user quit-vaping intentions that human evaluators might overlook. These preliminary findings emphasize the potential of GPT-4 in enhancing the accuracy and reliability of social media data analysis, especially in identifying subtle users' intentions that may elude human detection.<|reference_end|>
@article{vuruma2024utilizing,
    title={Utilizing Large Language Models to Identify Reddit Users Considering
  Vaping Cessation for Digital Interventions},
    author={Sai Krishna Revanth Vuruma, Dezhi Wu, Saborny Sen Gupta, Lucas Aust,
  Valerie Lookingbill, Caleb Henry, Yang Ren, Erin Kasson, Li-Shiun Chen,
  Patricia Cavazos-Rehg, Dian Hu, Ming Huang},
    journal={arXiv preprint arXiv:2404.17607},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17607},
    primaryClass={cs.IR cs.AI cs.CL cs.LG cs.SI}
}