A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health.
JAMIA Open
; 7(1): ooae001, 2024 Apr.
Article
de En
| MEDLINE
| ID: mdl-38250583
ABSTRACT
Objectives:
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). Materials andMethods:
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.Results:
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. Discussion andConclusion:
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Type d'étude:
Qualitative_research
Langue:
En
Journal:
JAMIA Open
Année:
2024
Type de document:
Article
Pays d'affiliation:
Canada
Pays de publication:
États-Unis d'Amérique