Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing.

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ID: 51374
2019
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Ranked #6 of 170 articles by views in Studies in health technology and informatics

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Abstract
Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients. All participants completed the four-item Centers for Disease Control Healthy Days Questionnaire at the time of enrollment and 30 days later to measure "ground truth" HRQOL. We used a combination of document frequency analysis, sentiment analysis, topic analysis, and concept mapping to extract features from tweets, which we then used to estimate dichotomized HRQOL ("high" vs. "low") using logistic regression. Binary HRQOL status was estimated with moderate performance (AUC = 0.64). This result indicates that free-range social media data only offers a window into HRQOL, but does not afford direct access to current health status.
Reference Key
sarma2019estimatingstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sarma, Karthik V;Spiegel, Brennan M R;Reid, Mark W;Chen, Shawn;Merchant, Raina M;Seltzer, Emily;Arnold, Corey W;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190388
URL
Keywords
social media quality of life natural language processing

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