Detection of Suicide-Related Posts in Twitter Data Streams

Mia Johnson Vioulès 1 Bilel Moulahi 1 Jérôme Azé 1 Sandra Bringay 1, 2
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 MIAP - Département de Mathématiques et Informatique Appliquées
Abstract : Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information spread across social media platforms holds valuable indicators to effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide-warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.
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Submitted on : Wednesday, November 22, 2017 - 10:59:51 AM
Last modification on : Thursday, June 6, 2019 - 2:43:54 PM

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LIRMM | CNRS | ADVANSE | MIPS | UNIV-MONTPELLIER | UNIV-MONTP3

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Mia Johnson Vioulès, Bilel Moulahi, Jérôme Azé, Sandra Bringay. Detection of Suicide-Related Posts in Twitter Data Streams. Ibm Journal of Research and Development, Ibm Corporation, 2018, 62 (1), pp.7:1-7:12. ⟨10.1147/JRD.2017.2768678⟩. ⟨lirmm-01633317⟩

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