Abstract
Automatically detect suicidal people in social networks is a real social issue. In France, suicide attempt is an economic burden with strong socio-economic consequences. In this paper, we describe a complete process to automatically collect suspect tweets according to a vocabulary of topics suicidal persons are used to talk. We automatically capture tweets indicating suicidal risky behaviour based on simple classification methods. An interface for psychiatrists has been implemented to enable them to consult suspect tweets and profiles associated with these tweets. The method has been validated on real datasets. The early feedback of psychiatrists is encouraging and allow to consider a personalised response according to the estimated level of risk.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Gunn, J., Lester, D.: Twitter postings and suicide: An analysis of the postings of a fatal suicide in the 24 hours prior to death. Suicidologi 17(3), 28–30 (2012)
Luyckx, K., Vaassen, F., Peersman, C., Daelemans, W.: Fine-grained emotion detection in suicide notes: A thresholding approach to multi-label classification. Biomed Inform. Insights 5(1), 61–69 (2012)
Roche, M., Garbasevschi, O.M.: WeMiT: Web-Mining for Translation. In: Conference on Prestigious Applications of Intelligent Systems, Montpellier, France, pp. 993–994 (August 2012)
Mohammad, S.M., Turney, P.D.: Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon. In: Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34. ACL, Stroudsburg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Abboute, A., Boudjeriou, Y., Entringer, G., Azé, J., Bringay, S., Poncelet, P. (2014). Mining Twitter for Suicide Prevention. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-07983-7_36
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07982-0
Online ISBN: 978-3-319-07983-7
eBook Packages: Computer ScienceComputer Science (R0)