@inproceedings{989acb247d1148fa866834cde5cdd560,
title = "Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale",
abstract = "Suicide is a leading cause of death in the United States. One of the major challenges to suicide prevention is that those who may be most at risk cannot be relied upon to report their conditions to clinicians. This paper takes an initial step toward the automatic detection of suicidal risk factors through social media activity, with no reliance on self-reporting. We consider the performance of annotators with various degrees of expertise in suicide prevention at annotating microblog data for the purpose of training text-based models for detecting suicide risk behaviors. Consistent with crowdsourcing literature, we found that novice-novice annotator pairs underperform expert annotators and outperform automatic lexical analysis tools, such as Linguistic Inquiry and Word Count.",
author = "Homan, {Christopher M.} and Ravdeep Johar and Tong Liu and Megan Lytle and Vincent Silenzio and Alm, {Cecilia O.}",
note = "Publisher Copyright: {\textcopyright} 2014 Association for Computational Linguistics; 2014 Workshop on Computational Linguistics and Clinical Psychology From Linguistic Signal to Clinical Reality, CLPsych 2014 at the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 ; Conference date: 27-06-2014",
year = "2014",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "107--117",
editor = "Philip Resnik and Rebecca Resnik and Margaret Mitchell",
booktitle = "Workshop on Computational Linguistics and Clinical Psychology From Linguistic Signal to Clinical Reality, CLPsych 2014 at the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings",
}