心理健康
心理学
集合(抽象数据类型)
心理干预
认知
风格(视觉艺术)
社会心理学
应用心理学
精神科
计算机科学
历史
考古
程序设计语言
作者
Rebecca K. Britt,Heather J. Carmack,Andrew Morris,Ananya Raka Chakraborty,Courtny L. Franco
标识
DOI:10.1080/10810730.2023.2278609
摘要
The present study investigated the latent topics and language styles present in mental health organizational discourse on Twitter. The researchers sought to analyze identifying the prevalence of and language used in social support messaging in tweets about mental health care, the overarching topics regarding mental health care, and predicted that tweets with higher engagement will have increased frequency of words with positively valenced emotion and cognitive processing. A GSDMM was run to uncover latent themes that emerged in a data set of 326.9k tweets and 7.2 m words about organizational discussions of mental health. A generalized linear model using the Poisson distribution was used to assess the role of engagement, positive emotion, and cognitive processing. The study found support for both positive emotion and cognitive processing as statistically significant predictors of engagement. Directions for research include the development of health message strategies, policy needs, and online interventions.
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