在社交媒体帖子中改善自杀念头检测:主题建模和合成数据增强方法
Hamideh Ghanadian1, Isar Nejadgholi2, Hussein Al Osman1
1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.
JMIR formative research
|June 11, 2025
概括
合成数据生成改进了人工智能模型,用于检测在线自杀讨论,特别是代表性不足的群体. 这种方法提高了主题多样性和确定社交媒体对话中关键风险因素的准确性.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 公共卫生信息学 公共卫生信息学
背景情况:
- 在线自杀讨论对公共卫生至关重要,但往往缺乏边缘化社区的代表性.
- 在社交媒体数据中的代表性不足导致AI模型在关键主题上的表现不佳.
研究的目的:
- 分析在线自杀讨论,并确定代表性不足的主题.
- 为人工智能培训生成合成数据,以提高自杀相关数据集的多样性和覆盖范围.
主要方法:
- 在社交媒体数据上进行无监督和指导的主题建模.
- 对自杀风险因素的心理学文献的综述.
- 生成大型语言模型 (GPT-3.5 Turbo) 用于合成数据的创建.
- 对合成数据的可读性,复杂性和实用性的评估.
主要成果:
- 关键话题,特别是那些涉及边缘化社区和种族主义的话题,在真实的社交媒体数据中表现不足.
- 合成数据生成改善了主题多样性和增强数据集.
- 微调人工智能分类器与增强数据显著改善了自杀念头检测准确度 (F1分数从0.70到0.90).
结论:
- 合成数据集对于理解在线自杀话语是有价值的.
- 用合成数据增强训练的人工智能模型在检测自杀叙事时显示出更高的准确性.
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