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Millions of users share their experiences on social media sites, such asTwitter, 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 apersonal health event was mentioned, which we call the (PHM) problem. This taskis challenging for many reasons, including typically short length of socialmedia posts, inventive spelling and lexicons, and figurative language,including hyperbole using diseases like "heart attack" or "cancer" foremphasis, and not as a health self-report. This problem is even morechallenging for rarely reported, or frequent but ambiguously expressedconditions, such as "stroke". To address this problem, we propose a general,robust method for detecting PHMs in social media, which we call WESPAD, thatcombines lexical, syntactic, word embedding-based, and context-based features.WESPAD is able to generalize from few examples by automatically distorting theword embedding space to most effectively detect the true health mentions.Unlike previously proposed state-of-the-art supervised and deep-learningtechniques, WESPAD requires relatively little training data, which makes itpossible to adapt, with minimal effort, to each new disease and condition. Weevaluate WESPAD on both an established publicly available Flu detectionbenchmark, and on a new dataset that we have constructed with mentions ofmultiple health conditions. Our experiments show that WESPAD outperforms thebaselines and state-of-the-art methods, especially in cases when the number andproportion of true health mentions in the training data is small.

Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media