This article is a system description and report on the submission of the World Well-Being Project from the University of Pennsylvania in the 'CLPsych 2015' shared task. The goal of the shared task was to automatically determine Twitter users who self-reported having one of two mental illnesses: post traumatic stress disorder (PTSD) and depression. Our system employs user metadata and textual features derived from Twitter posts. To reduce the feature space and avoid data sparsity, we consider several word clustering approaches. We explore the use of linear classifiers based on different feature sets as well as a combination use a linear ensemble. This method is agnostic of illness specific features, such as lists of medicines, thus making it readily applicable in other scenarios. Our approach ranked second in all tasks on average precision and showed best results at .1 false positive rates.
REF employed user metadata and textual features from the corpus provided by the CLPsych 2015 Shared Task to develop a linear classifier to predict users having either one of the mental illnesses.
Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task
CLPsych@HLT-NAACL
Computer Science