Does Organizational Messaging Make a Difference? Investigating Themes and Language Style in Twitter Discourse and Engagement by Mental Health Organizations.

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-01-02 Epub Date: 2024-01-09 DOI:10.1080/10810730.2023.2278609

Rebecca K Britt, Heather J Carmack, Andrew Morris, Ananya Raka Chakraborty, Courtny L Franco

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Abstract

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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组织信息传递有影响吗?心理健康组织在推特话语和参与中调查主题和语言风格。

本研究调查了Twitter上心理健康组织话语中的潜在话题和语言风格。研究人员试图分析确定推特中关于精神卫生保健的社会支持信息的流行程度和使用的语言，这是关于精神卫生保健的首要主题，并预测参与度较高的推特将增加具有积极价值的情感和认知处理的单词频率。运行GSDMM是为了发现在关于心理健康的组织讨论的326.9万条推文和720万个单词的数据集中出现的潜在主题。使用泊松分布的广义线性模型来评估参与、积极情绪和认知加工的作用。该研究发现，积极情绪和认知处理都是敬业度的统计显著预测因素。研究方向包括制定卫生信息战略、政策需求和在线干预措施。

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