在挖掘女性社会内容中的一个预防性目标是使用软计算深度框架识别抑郁症的早期迹象
Hanen Karamti1, Abeer M Mahmoud2
1Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, Kingdom of Saudi Arabia.
Scientific reports
|September 9, 2023
概括
这项研究引入了一个深度学习框架,通过社交媒体帖子检测女性的抑郁症. 该模型从文本中准确识别抑郁症指标,为心理健康研究提供了一种新的方法.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 机器学习用于社会科学.
背景情况:
- 社交媒体每天都会产生大量用户生成的内容.
- 分析这些数据可以在包括心理健康在内的各个领域做出有价值的发现.
- 早期发现女性抑郁症对于及时干预至关重要.
研究的目的:
- 提出一个深度学习框架,以准确检测女性的抑郁症.
- 利用社交媒体内容和心理语言指标来识别抑郁症状.
- 通过各种数据集验证模型的有效性.
主要方法:
- 开发了一个深度学习框架,利用社交媒体帖子 (推文) 和心理语言分析.
- 从抑郁症指标词汇库创建了词嵌入.
- 在两个推特数据集 (全球700名女性,沙特阿拉伯80名女性) 和CLPsych 2015基准数据集上验证了该模型.
主要成果:
- 拟议的深度学习模型在所有三个验证数据集中都表现出有效的性能.
- 该框架成功地从社交媒体内容中识别了抑郁症的迹象.
- 结果表明该模型在检测女性抑郁症方面的稳定性和准确性.
结论:
- 深度学习框架提供了一种有效的方法,通过社交媒体分析来检测女性的抑郁症.
- 心理语言支持增强了模型识别微妙抑郁症指标的能力.
- 这种方法具有大规模,可访问的心理健康监测的潜力.
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