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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
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Iterative Large Language Model-Guided Sampling and Expert-Annotated Benchmark Corpus for Harmful Suicide Content

Kyumin Park1, Myung Jae Baik2, YeongJun Hwang3

  • 1SoftlyAI, Seoul, Republic of Korea.

JMIR Medical Informatics
|February 5, 2026
PubMed
Summary
This summary is machine-generated.

This study developed an AI system to classify harmful online suicide content, creating a benchmark dataset to improve detection and moderation. The AI shows promise in identifying dangerous material, aiding efforts to protect vulnerable users.

Keywords:
artificial intelligencedatasetlarge language modelssuicidesuicide-related content

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Area of Science:

  • Artificial Intelligence
  • Computational Linguistics
  • Public Health

Background:

  • Online suicide content poses risks, especially in high-suicide-rate countries like South Korea.
  • Current moderation struggles to assess content harmfulness, focusing instead on author risk.
  • A critical gap exists in evaluating content's potential to induce suicidal ideation in readers.

Purpose of the Study:

  • Develop an AI system to classify online suicide content into five levels: illegal, harmful, potentially harmful, harmless, and non-suicide-related.
  • Create a multimodal benchmark dataset with expert annotations for improved AI-driven content moderation.
  • Enhance the detection and regulation of harmful online content.

Main Methods:

  • Collected 43,244 user-generated posts from diverse online platforms.
  • Utilized GPT-4 for preannotation and filtering, followed by manual review by medical professionals.
  • Developed a 452-entry multimodal benchmark dataset in Korean and English, evaluating zero-shot and few-shot learning approaches.

Main Results:

  • GPT-4 achieved high F1-scores (66.46 for illegal, 77.09 for harmful content).
  • Image descriptions boosted classification accuracy; raw images slightly decreased performance.
  • Few-shot learning significantly improved detection, highlighting the value of high-quality datasets.

Conclusions:

  • A high-quality benchmark for AI-based suicide content detection was established.
  • Large language models can effectively assist in content moderation, reducing human moderator workload.
  • Future research will focus on real-time detection and handling subtle or disguised harmful content.