Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language
Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language
复制标题
小而强大：从社交媒体语言量化心理健康的情感微模式
DOI：
10.18653/v1/w17-3110
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Glen A. Coppersmith
中科院分区：
文献类型：
--
作者：
Kate Loveys;P. Crutchley;Emily Wyatt;Glen A. Coppersmith
许多心理现象发生在以分钟或小时为单位的小时间窗口内。然而，大多数计算语言学技术都是以周、月或年为单位来看待数据的。我们探索在短时间内发生的消息序列中的微模式，以了解它们的流行程度和量化心理现象的能力，特别是影响模式。我们研究了社交媒体帖子中的情感微模式，这些帖子来自焦虑，饮食失调，恐慌发作，精神分裂症，自杀倾向和匹配的对照组。
Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.
影响因子：
12.8
作者：
Messinger, Julie W.;Tremeau, Fabien;Antonius, Daniel;Mendelsohn, Erika;Prudent, Vasthie;Stanford, Arielle D.;Malaspina, Dolores
通讯作者：
Malaspina, Dolores