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Optimizing Online Suicide Prevention: A Search Engine-Based Tailored Approach.

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Area of Science:

  • Digital Health
  • Public Health
  • Computational Social Science

Background:

  • Online search engines are crucial for accessing suicide-related information.
  • Current suicide prevention interventions by search engines are limited in reach.
  • There's a need for more effective, targeted online suicide prevention strategies.

Purpose of the Study:

  • To propose a tailored optimization approach for search engine suicide prevention results.
  • To identify specific risk factors correlating with online search behavior for suicide.
  • To enhance the effectiveness of search engine interventions for vulnerable populations.

Main Methods:

  • Analyzing search query data, specifically for terms like "poisoning", to identify temporal patterns.
  • Correlating search query peaks with known periods of increased suicidal behavior.
  • Proposing a two-step optimization: risk factor identification and algorithm reweighting.

Main Results:

  • Significant peaks in "poisoning" search queries correspond with actual increases in suicidal behavior.
  • Specific times, including spring, Sundays, Mondays, New Year's Day, and the Saturday after Thanksgiving, show elevated risk.
  • Current search engine interventions miss opportunities to help vulnerable users.

Conclusions:

  • Search engines can significantly contribute to global suicide prevention through tailored algorithms.
  • Adjusting the display thresholds for suicide prevention results based on identified risk factors is feasible.
  • A data-driven, risk-aware approach can optimize the impact of online suicide prevention efforts.