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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.17607 |
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| _version_ | 1866911856714579968 |
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| author | Vuruma, Sai Krishna Revanth Wu, Dezhi Gupta, Saborny Sen Aust, Lucas Lookingbill, Valerie Henry, Caleb Ren, Yang Kasson, Erin Chen, Li-Shiun Cavazos-Rehg, Patricia Hu, Dian Huang, Ming |
| author_facet | Vuruma, Sai Krishna Revanth Wu, Dezhi Gupta, Saborny Sen Aust, Lucas Lookingbill, Valerie Henry, Caleb Ren, Yang Kasson, Erin Chen, Li-Shiun Cavazos-Rehg, Patricia Hu, Dian Huang, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17607 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Utilizing Large Language Models to Identify Reddit Users Considering Vaping Cessation for Digital Interventions Vuruma, Sai Krishna Revanth Wu, Dezhi Gupta, Saborny Sen Aust, Lucas Lookingbill, Valerie Henry, Caleb Ren, Yang Kasson, Erin Chen, Li-Shiun Cavazos-Rehg, Patricia Hu, Dian Huang, Ming Information Retrieval Artificial Intelligence Computation and Language Machine Learning Social and Information Networks 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. |
| title | Utilizing Large Language Models to Identify Reddit Users Considering Vaping Cessation for Digital Interventions |
| topic | Information Retrieval Artificial Intelligence Computation and Language Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2404.17607 |