Identifying Dietary Supplements Related Effects from Social Media by ChatGPT.

Abstract

This study advances relationship identification in social media by analyzing dietary supplement-related tweets aiming to expand the drug-supplement interactions dataset iDisk. We collected 90,000+ tweets (2007-2022) and annotated 1,000 for nuanced relationships and entities. Using a BioBERT model and ChatGPT-generated prompts, we conducted entity type and relationship identification. The BioBERT model achieved an F1 score of 0.90 for relationship prediction, while ChatGPT prompts reached 0.99. Entity type recognition proved more challenging, with high semantic similarity between types impacting accuracy. Our methodology significantly enhances relationship identification from social media data, particularly for dietary supplements usage, offering promising methods for improved post-market surveillance and public health monitoring. This work demonstrates the potential of combining traditional NLP models with large language models for complex text analysis tasks in healthcare.