Exploring How Healthcare Organizations Use Twitter: A Discourse Analysis

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Informatics Pub Date : 2023-08-08 DOI:10.3390/informatics10030065
Aditya Singhal, Vijay K. Mago
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Abstract

The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse analysis to better understand how public and private healthcare organizations use Twitter and the factors that influence the content of their tweets. Data were collected from the Twitter accounts of five private pharmaceutical companies, two US and two Canadian public health agencies, and the World Health Organization from 1 January 2020, to 31 December 2022. The study applied topic modeling and association rule mining to identify text patterns that influence the content of tweets across different Twitter accounts. The findings revealed that building a reputation on Twitter goes beyond just evaluating the popularity of a tweet in the online sphere. Topic modeling, when applied synchronously with hashtag and tagging analysis can provide an increase in tweet popularity. Additionally, the study showed differences in language use and style across the Twitter accounts’ categories and discussed how the impact of popular association rules could translate to significantly more user engagement. Overall, the results of this study provide insights into natural language processing for health literacy and present a way for organizations to structure their future content to ensure maximum public engagement.
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探索医疗机构如何使用Twitter:一个话语分析
医疗机构使用推特是向公众传播医疗信息的有效手段。然而,推特的内容可能受到各种因素的影响,例如突发卫生事件和医疗突破。在这项研究中,我们进行了一项话语分析,以更好地了解公共和私人医疗机构如何使用推特,以及影响其推文内容的因素。从2020年1月1日至2022年12月31日,数据来自五家私营制药公司、两家美国和两家加拿大公共卫生机构以及世界卫生组织的推特账户。该研究应用主题建模和关联规则挖掘来识别影响不同推特账户推文内容的文本模式。调查结果显示,在推特上建立声誉不仅仅是评估推特在网络领域的受欢迎程度。当主题建模与标签和标签分析同步应用时,可以提高推特的受欢迎程度。此外,该研究显示了推特账户类别中语言使用和风格的差异,并讨论了流行关联规则的影响如何转化为显著提高用户参与度。总的来说,这项研究的结果为健康素养的自然语言处理提供了见解,并为组织提供了一种构建未来内容的方法,以确保最大限度的公众参与。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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