Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis.

ABSTRACT:

Background

Tweets can provide broad, real-time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known, however, about how much individuals post about common health conditions or what they post about.

Objective

We sought to collect and analyze tweets from 1 state about high prevalence health conditions and characterize the tweet volume and content.

Methods

We collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the cont

SUBMITTER: Tufts C 

PROVIDER: S-EPMC6302232 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature