  Millions of users share their experiences on social media sites, such as
Twitter, which in turn generate valuable data for public health monitoring,
digital epidemiology, and other analyses of population health at global scale.
The first, critical, task for these applications is classifying whether a
personal health event was mentioned, which we call the (PHM) problem. This task
is challenging for many reasons, including typically short length of social
media posts, inventive spelling and lexicons, and figurative language,
including hyperbole using diseases like "heart attack" or "cancer" for
emphasis, and not as a health self-report. This problem is even more
challenging for rarely reported, or frequent but ambiguously expressed
conditions, such as "stroke". To address this problem, we propose a general,
robust method for detecting PHMs in social media, which we call WESPAD, that
combines lexical, syntactic, word embedding-based, and context-based features.
WESPAD is able to generalize from few examples by automatically distorting the
word embedding space to most effectively detect the true health mentions.
Unlike previously proposed state-of-the-art supervised and deep-learning
techniques, WESPAD requires relatively little training data, which makes it
possible to adapt, with minimal effort, to each new disease and condition. We
evaluate WESPAD on both an established publicly available Flu detection
benchmark, and on a new dataset that we have constructed with mentions of
multiple health conditions. Our experiments show that WESPAD outperforms the
baselines and state-of-the-art methods, especially in cases when the number and
proportion of true health mentions in the training data is small.
