Analyzing Dementia Caregivers' Experiences on Twitter: A Term-Weighted Topic Modeling Approach.

Abstract

Dementia profoundly impacts patients and their families, making it essential to understand the experiences and concerns offamily caregivers for enhanced support and care. This study introduces a novel approach to analyzing tweets from individuals whose family members suffer from dementia. We preprocessed our collected Twitter (now X) data using advanced natural language processing techniques and enhanced conventional topic model-Gibbs Sampling Dirichlet Multinomial Mixture Model (GSDMM)-with term-weighting strategies to improve topic clarity. This enhanced approach enabled the identification of key topics among dementia-affected families, offering semantically rich and contextually coherent topics, demonstrating that our method outperforms the state-of-the-art BERTopic model in clarity and consistency. Leveraging ChatGPT 4 alongside two human experts, we uncovered the multifaceted challenges faced by family caregivers. This work aims to provide healthcare professionals, researchers, and support organizations with a valuable tool to better understand and address the needs offamily caregivers.