Mining Twitter to Inform the Design of Online Interventions for Hispanic Alzheimer's Disease and Related Dementias Caregivers.

Abstract

Background/objective: Hispanics are about 1.5 times as likely as non-Hispanic Whites to experience Alzheimer's disease and related dementias (AD/ADRD). Eight percent of AD/ADRD caregivers are Hispanics. The purpose of this article is to provide a methodological case study of using data mining methods and the Twitter platform to inform online self-management and social support intervention design and evaluation for Hispanic AD/ADRD caregivers. It will enable other researchers to replicate the methods for their phenomena of interest.

Method: We extracted an analytic corpus of 317,658 English and Spanish tweets, applied content mining (topic models) and network structure analysis (macro-, meso-, and micro-levels) methods, and created visualizations of results.

Results: The topic models showed differences in content between English and Spanish tweet corpora and between years analyzed. Our methods detected significant structural changes between years including increases in network size and subgroups, decrease in proportion of isolates, and increase in proportion of triads of the balanced communication type.

Discussion/conclusion: Each analysis revealed key lessons that informed the design and/or evaluation of online self-management and social support interventions for Hispanic AD/ADRD caregivers. These lessons are relevant to others wishing to use Twitter to characterize a particular phenomenon or as an intervention platform.