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Detecting Suicidal Ideation on Forums: Proof-of-Concept Study.

Ahmet Emre Aladağ1,2, Serra Muderrisoglu3, Naz Berfu Akbas4

  • 1Department of Computer Engineering, Bogazici University, Istanbul, Turkey.

Journal of Medical Internet Research
|June 23, 2018
PubMed
Summary

Researchers developed a text classifier to detect suicidal posts on online forums with high accuracy. This tool can identify individuals with suicidal ideation, potentially enabling real-time intervention and support.

Keywords:
artificial intelligenceclassification modeldetectionmachine learningpreventionsuicidal ideationsuicidal surveillancesuicidalitysuicidetext mining

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Area of Science:

  • Computational linguistics
  • Natural language processing
  • Machine learning for mental health

Background:

  • Suicide is a significant public health issue, with many individuals expressing suicidal ideation online before attempting suicide.
  • Online platforms like Reddit and blogs serve as spaces for individuals to share their feelings, often using discernible language patterns.
  • Early detection of suicidal content in online posts can facilitate timely intervention and prevention efforts.

Purpose of the Study:

  • To develop and evaluate a text classifier capable of distinguishing between suicidal and non-suicidal posts on online forums.
  • To apply text mining techniques to analyze post titles and bodies for indicators of suicidal ideation.

Main Methods:

  • A dataset of over 500,000 Reddit posts was collected, with 785 posts manually annotated for suicidality.
  • Features were extracted using term frequency-inverse document frequency (TF-IDF), linguistic inquiry and word count (LIWC), and sentiment analysis.
  • Logistic regression, random forest, and support vector machine (SVM) algorithms were employed for classification.

Main Results:

  • Logistic regression and SVM classifiers achieved high accuracy (80%-92%) and F1 scores in identifying suicidal posts.
  • These models significantly outperformed the baseline ZeroR algorithm (50% accuracy, 66% F1 score).

Conclusions:

  • It is feasible to accurately detect suicidal ideation in online forum posts using text mining and machine learning.
  • The developed logistic regression classifier shows potential for real-time integration into online platforms to offer immediate support to users exhibiting suicidal ideation.