Characterizing Suicide Ideation by Using Mental Disorder Features on Microblogs: A Machine Learning Perspective

Samer Sarsam, Hosam Al-Samarraie, Ahmed Ibrahim Alzahrani, Chit Su Mon, Abdul Samad Shibghatullah

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
22 Downloads (Pure)


Despite the success of psychological and clinical methods, psychological studies revealed that the number of individuals exhibiting suicide ideation has highly increased in the recent decades. This study explored the potential of using certain sentimental features as a means for characterizing suicide. A total of 54,385 English-language tweets were collected and processed to extract suicide-related topics using the Latent Dirichlet Allocation (LDA) algorithm. Both suicidal polarity (positive, negative, and neutral) and emotions (anger, fear, sadness, and trust) were extracted via SentiStrength, time series, and NRC Affect Intensity Lexicon methods. The results showed that suicidal tweets were less associated with trust, anger, and positive sentiments. In contrast, fear, sadness, and negative sentiments were highly associated with suicidal statements. The prediction results using this approach showed 97.64% accuracy in detecting suicide ideation. The obtained results from analyzing suicide-related tweets hold a promising future for characterizing suicide ideation worldwide.
Original languageEnglish
Pages (from-to)(In-Press)
Number of pages14
JournalInternational Journal of Mental Health and Addiction
Early online date14 Nov 2022
Publication statusE-pub ahead of print - 14 Nov 2022

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.


This work was funded by the Researchers Supporting Project number [RSP-2021/157], King Saud University, Riyadh, Saudi Arabia


  • Suicide ideation
  • Sentiment analysis
  • Topic modeling
  • Twitter


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