预测社交媒体文本中对自杀的负面态度:预测模型开发和验证研究
1Department of Psychology, Beijing Forestry University, Beijing, China.
Frontiers in public health
|July 23, 2024
概括
机器学习有效地从社交媒体数据中预测了对自杀的负面态度. 在这些预测模型实际应用之前,外部验证至关重要.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 机器学习在公共卫生中的应用.
背景情况:
- 之前的研究不足地解决了各种负面态度,并且缺乏预测模型的外部验证.
- 开发准确的机器学习模型来预测与自杀相关的负面态度对于改善健康结果至关重要.
- 大规模的社交媒体分析为了解和预测这些态度提供了一个有希望的途径.
研究的目的:
- 开发一个全面的机器学习模型来预测对自杀的负面态度.
- 通过分析大量社交媒体数据集,涵盖广泛的负面态度.
- 在人口和个人层面严格验证模型的性能.
主要方法:
- 分析了2009年至2020年的938,866条新浪微博帖子.
- 人类和基于计算机的内容分析用于特征提取和模型开发在10,000个帖子上.
- 对剩余职位 (人口水平) 的外部验证和单独的数据集 (个人水平).
主要成果:
- 模型开发实现了高性能,F1=0.93和AUC=0.97.
- 人口层面的验证显示了负面态度预测的统计学上显著但效果大小小小.
- 个人级别验证结果为F1=0.76和AUC=0.74.
结论:
- 机器学习对负面态度的预测是有效的,对公共卫生是必要的.
- 在实际实施之前,预测模型的外部验证是必不可少的.
- 这项研究强调了社交媒体数据在心理健康研究中的潜力.
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