Craig Harman

TL;DR: A novel method for gathering data for a range of mental illnesses quickly and cheaply is presented, then analysis of four in particular: post-traumatic stress disorder, depression, bipolar disorder, and seasonal affective disorder are focused on.

TL;DR: PTSD is considered, a serious condition that affects millions worldwide, with especially high rates in military veterans, and its utility is demonstrated by examining differences in language use between PTSD and random individuals, building classifiers to separate these two groups and by detecting elevated rates of PTSD at and around U.S. military bases using classifiers.

TL;DR: A broad range of mental health conditions in Twitter data is examined by identifying self-reported statements of diagnosis and language differences between ten conditions with respect to the general population, and to each other are systematically explored.

TL;DR: This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks, aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media.

TL;DR: The first large-scale, corpus based verification of Dowty’s seminal theory of proto-roles is presented, demonstrating both the need for and the feasibility of a property-based annotation scheme of semantic relationships.