Background: Twitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language.
Objective: We characterized the extent of these biases and how they vary with disease.
Methods: We correlated self-reported prevalence rates for 22 diseases from Experian's Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, "heart attack" on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease's overrepresentation or underrepresentation on Twitter, relative to its prevalence.
Results: Our sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89% (3827/25,704) of instances (for stroke) to 99.92% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of .208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to .366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most underrepresented.
Conclusions: Twitter is a potentially useful tool to measure public interest in and concerns about different diseases, but when comparing diseases, improvements can be made by adjusting for population demographics and word ambiguity.
Keywords: bias; data mining; demographics; disease; epidemiology; prevalence; public health; social media.