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Logistic regression analysis of textual data on suicidal ideation.

Takafumi Kubota1, Takahiro Arai1,2

  • 1School of Information and Management Sciences, Tama University, Tama, Tokyo, Japan.

PLOS Mental Health
|February 9, 2026
PubMed
Summary

Understanding suicidal ideation factors is key for prevention. This study found that women and younger individuals, particularly at night, express more "self/identity" concerns, highlighting nighttime as a critical period for heightened suicidal thoughts.

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Area of Science:

  • Psychiatry
  • Public Health
  • Computational Social Science

Background:

  • Suicide prevention necessitates understanding the full spectrum from ideation to death.
  • Identifying factors influencing suicidal ideation is crucial for early intervention.

Purpose of the Study:

  • To identify factors associated with suicidal ideation using online textual data.
  • To analyze demographic and temporal influences on the content of messages related to suicide.

Main Methods:

  • Logistic regression analysis applied to user-generated text data from NHK's "Facing Suicide" website (September 2024).
  • Keywords were categorized, and demographic (gender, age) and temporal (day, time) factors were examined.

Main Results:

  • Demographic and temporal factors significantly influenced message content.
  • Women and younger individuals were more prone to posting messages focused on "self/identity" and "functional words/actions."
  • Concerns related to self-perception intensified during late-night hours.

Conclusions:

  • Nighttime emerges as a critical period for increased suicidal ideation.
  • Understanding these temporal patterns can inform targeted suicide prevention strategies.
  • Online platforms offer valuable data for studying mental health trends.