Citation

Grant RN, Kucher D, Leon AM, Gemmell JF, Raicu DS, Fodeh SJ. BMC Bioinformatics 2018; 19(Suppl 8): e211.

Affiliation

Yale Center for Medical Informatics, Yale University, New Haven, CT, USA.

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12859-018-2197-z

PMID

29897319

Abstract

BACKGROUND: Suicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide. Many more individuals contemplate suicide. Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem. As social networking sites have become more common, users have adopted these sites to talk about intensely personal topics, among them their thoughts about suicide. Such data has previously been evaluated by analyzing the language features of social media posts and using factors derived by domain experts to identify at-risk users.

RESULTS: In this work, we automatically extract informal latent recurring topics of suicidal ideation found in social media posts. Our evaluation demonstrates that we are able to automatically reproduce many of the expertly determined risk factors for suicide. Moreover, we identify many informal latent topics related to suicide ideation such as concerns over health, work, self-image, and financial issues.

CONCLUSIONS: These informal topics topics can be more specific or more general. Some of our topics express meaningful ideas not contained in the risk factors and some risk factors do not have complimentary latent topics. In short, our analysis of the latent topics extracted from social media containing suicidal ideations suggests that users of these systems express ideas that are complementary to the topics defined by experts but differ in their scope, focus, and precision of language.

Language: en

Keywords

Suicidal ideation; Text mining; Word2Vec