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.
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© 2014 Springer International Publishing Switzerland
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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
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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
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