A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health

• Published in: JAMIA open Vol. 7; no. 1; p. ooae001
• Main Authors: Ruocco, Leonard, Zhuang, Yuqian, Ng, Raymond, Munthali, Richard J, Hudec, Kristen L, Wang, Angel Y, Vereschagin, Melissa, Vigo, Daniel V
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.04.2024
• Subjects: Artificial intelligence; Case studies; College students; Computational linguistics; Depression, Mental; Language processing; Mental health; Natural language interfaces; Psychological aspects; Social media; Canada; topic modeling; natural language processing; social media; mental health; artificial intelligence
• ISSN: 2574-2531; 2574-2531
• DOI: 10.1093/jamiaopen/ooae001

To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI). Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI. Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data. Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.