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在社交媒体上,负面影响的可变性在焦虑和抑郁之间有所不同
Lauren A Rutter1,2, Marijn Ten Thij1,3,4, Lorenzo Lorenzo-Luaces1,2
1Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America.
PloS one
|February 21, 2024
概括
患有抑郁症的人比患有焦虑症的人表现出更大的负面影响变化. 这项研究表明,社交媒体情绪分析可以有效地在规模上测量心理健康.
科学领域:
- 精神病学是一个精神病学.
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
背景情况:
- 负面影响的变化与内部化精神病理学有关,包括抑郁和焦虑.
- 对比回避模型 (CAM) 认为,焦虑涉及通过担忧避免负面情绪转变.
- 最近的研究表明,CAM可以应用于严重抑郁症和社会恐惧症,因为它们的特征是负面影响的变化.
研究的目的:
- 为了比较被诊断患有焦虑障碍和抑郁障碍的个体之间的负面影响变化.
- 调查在线沟通情绪分析对评估心理健康状况的有用性.
主要方法:
- 分析了1,853名被诊断患有焦虑症 (n=896) 或抑郁症 (n=957) 的个人推特通信.
- 使用情感推理的Valence Aware词典 (VADER) 计算了平均负面影响 (NA) 和NA可变性.
主要成果:
- 被诊断患有抑郁症的个人 (D队列) 比被诊断患有焦虑症的个人 (A队列) 显示出明显更高的NA变异性.
- 与A队列相比,D队列的总体平均NA也较高.
- 通过U统计,p值和效果大小 (r,d) 证实了统计显著性.
结论:
- 抑郁症诊断与比焦虑症更大的负面影响变化有关.
- 对大规模社交媒体数据的情绪分析为研究心理健康影响提供了一种可行的方法.
- 这种方法使得自然发生的心理健康现象能够进行可扩展的研究.
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