A Scalable Framework to Detect Personal Health Mentions on Twitter

Abstract

Background: Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual's health.

Objective: The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed.

Methods: We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status.

Results: Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author's status, while only 1% (1/100) disclosed another person's status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author's status, while 21% (21/100) disclosed another person's status.

Conclusions: It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.

Keywords: consumer health; infodemiology; information retrieval; machine learning; social media; twitter.

Figure 1

Framework for personal health mention detection over Twitter. First, tweets are filtered into bins according to health issue topic. A portion of the tweets are supplied to a labeling service. The labeled data is then applied to train a classifier to detect personal health mentions.

Figure 2

Label hierarchy.

Figure 3

Overview of evaluation strategies for the personal health status mention classifier. Note, D={d1, d2, …, dn} is set of health issues, X is set of health issues selected to train classifier, and Y is set of health issues used to test classifier.

Figure 4

The extent to which people tweet about themselves versus others when disclosing personal health status. Note that this is a stacked bar chart, such that the sum of the author and others proportions corresponds to the overall proportion of positive instances.

Figure 5

Comparison Between heterogeneous classifiers HEC-1 and HEC-N trained on cancer, depression, hypertension, and leukemia, and tested on the remaining 30 health issues. The tweets of each test health issue stratified with respect to their rate of observation.

Figure 6

PR (precision recall) curves for testing on the gold, CAN (conflict as negative), and CAP (conflict as positive) datasets.

Figure 7

Performance of the SYND (synthetic health issue) classifier with a varying amount of training data.