Computational Exploration of Gerontology-Related Topics Shared on Social Media Platform Twitter

Abstract

Twitter, a popular Internet social media platform, has become a significant medium for sharing information and ideas about various topics, including aging and older adults. While studies have been conducted to examine stigma or perception about specific diseases such as Alzheimer’s disease and seizure on Twitter, there has been little effort to identify general themes of Twitter posts related to aging and older adults. This exploratory study attempts to answer this need by conducting a topic analysis of posts shared on Twitter posts about aging and older adults in English. We collected 328,568 English posts from Twitter posted between 07/01/18 and 07/31/18 using 19 English keywords referring to older adults. We analyzed this whole dataset as well as a subset of posts explicitly including aging-related hashtags, such as #olderadults. We used data mining methods (including Latent Dirichlet Allocation) in computing environment MATLAB to discover topics emerging from these two sets. Among posts with explicit aging-related hashtags, the most recurrent topics were family (relation with children and grandchildren, commemoration), community (resources, looking after older adults), health (disease-specific, public health, home care, formal and informal caregivers), politics and policies (insurance/pension, new laws), and news involving older adults (e.g., crimes on/by older adults). The analysis of the larger dataset additionally uncovered posts promoting pornography featuring older females and posts sharing general Internet content featuring older adults (e.g., YouTube videos). We also share the methodological challenges we encountered and practical recommendations for gerontological researchers interested in using social media data to inform new research.