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Online suicide prevention through optimised text classification

Bart Desmet (UGent) and Veronique Hoste (UGent)
(2018) INFORMATION SCIENCES. 439. p.61-78
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Abstract
Online communication platforms are increasingly used to express suicidal thoughts. There is considerable interest in monitoring such messages, both for population-wide and individual prevention purposes, and to inform suicide research and policy. Online information overload prohibits manual detection, which is why keyword search methods are typically used. However, these are imprecise and unable to handle implicit references or linguistic noise. As an alternative, this study investigates supervised text classification to model and detect suicidality in Dutch-language forum posts. Genetic algorithms were used to optimise models through feature selection and hyperparameter optimisation. A variety of features was found to be informative, including token and character ngram bags-of-words, presence of salient suicide-related terms and features based on LSA topic models and polarity lexicons. The results indicate that text classification is a viable and promising strategy for detecting suicide-related and alarming messages, with F-scores comparable to human annotators (93% for relevant messages, 70% for severe messages). Both types of messages can be detected with high precision and minimal noise, even on large high-skew corpora. This suggests that they would be fit for use in a real-world prevention setting.
Keywords
Suicide prevention, Social media, Text classification, Machine learning, Feature selection, Optimisation

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Citation

Please use this url to cite or link to this publication:

Chicago
Desmet, Bart, and Veronique Hoste. 2018. “Online Suicide Prevention Through Optimised Text Classification.” Information Sciences 439: 61–78.
APA
Desmet, B., & Hoste, V. (2018). Online suicide prevention through optimised text classification. INFORMATION SCIENCES, 439, 61–78.
Vancouver
1.
Desmet B, Hoste V. Online suicide prevention through optimised text classification. INFORMATION SCIENCES. Elsevier; 2018;439:61–78.
MLA
Desmet, Bart, and Veronique Hoste. “Online Suicide Prevention Through Optimised Text Classification.” INFORMATION SCIENCES 439 (2018): 61–78. Print.
@article{8521331,
  abstract     = {Online communication platforms are increasingly used to express suicidal thoughts. There is considerable interest in monitoring such messages, both for population-wide and individual prevention purposes, and to inform suicide research and policy. Online information overload prohibits manual detection, which is why keyword search methods are typically used. However, these are imprecise and unable to handle implicit references or linguistic noise. As an alternative, this study investigates supervised text classification to model and detect suicidality in Dutch-language forum posts. Genetic algorithms were used to optimise models through feature selection and hyperparameter optimisation. A variety of features was found to be informative, including token and character ngram bags-of-words, presence of salient suicide-related terms and features based on LSA topic models and polarity lexicons. The results indicate that text classification is a viable and promising strategy for detecting suicide-related and alarming messages, with F-scores comparable to human annotators (93\% for relevant messages, 70\% for severe messages). Both types of messages can be detected with high precision and minimal noise, even on large high-skew corpora. This suggests that they would be fit for use in a real-world prevention setting.},
  author       = {Desmet, Bart and Hoste, Veronique},
  issn         = {0020-0255},
  journal      = {INFORMATION SCIENCES},
  language     = {eng},
  pages        = {61--78},
  publisher    = {Elsevier},
  title        = {Online suicide prevention through optimised text classification},
  url          = {http://dx.doi.org/10.1016/j.ins.2018.02.014},
  volume       = {439},
  year         = {2018},
}

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