通过社交媒体,在时间和空间中进行基于语言的强有力的心理健康评估
Siddharth Mangalik1, Johannes C Eichstaedt2,3, Salvatore Giorgi4
1Department of Computer Science, Stony Brook University, Stony Brook, NY, USA. smangalik@cs.stonybrook.edu.
NPJ digital medicine
|May 2, 2024
概括
社交媒体数据提供了一种强大的新方法来跟踪人口心理健康,比传统调查提供了更可靠,更详细的抑郁和焦虑洞察力.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生监督是对公共卫生的监督.
- 数字流行病学数字流行病学
背景情况:
- 当前人口心理健康调查的细节性和可靠性有限.
- 估计经常汇总到年度州级数据中,掩盖更细微的变化.
- 现有的方法很难捕捉抑郁症和焦虑症等心理健康状况的细微差别.
研究的目的:
- 开发和验证一种使用社交媒体数据估计人口心理健康的新方法.
- 以高时空分辨率评估心理健康水平和抑郁症和焦虑症的变化.
- 将基于社交媒体的评估与传统调查措施的可靠性和有效性进行比较.
主要方法:
- 利用一条管道分析了来自200万地理位置用户的约10亿条推文.
- 开发了基于语言的心理健康评估 (LBMHA) 来估计抑郁和焦虑.
- 根据现有调查数据验证的LBMHA,以各种细节 (从国家到县周) 进行验证.
主要成果:
- 与调查措施相比,LBMHAs在空间和时间上表现出卓越的可靠性.
- 对抑郁症和焦虑的可靠评估已经达到县周水平.
- LBMHAs显示了与调查得分的中度至强度的关联,以及与健康/社会经济指标的改善外部有效性.
结论:
- 大规模的社交媒体数据分析使得强大的,高分辨率的人口心理健康估计.
- 基于语言的心理健康评估为传统调查提供了更细致,可靠和有效的替代方案.
- 这种方法为公共卫生监测和干预策略提供了宝贵的见解.
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