Ola Oluwakemi, Sedig Kamran
1University of British Columbia, 2Western University.
Online J Public Health Inform. 2020 May 16;12(1):e2. doi: 10.5210/ojphi.v12i1.10321. eCollection 2020.
Social media allows for the exploration of online discussions of health issues outside of traditional health spaces. Twitter is one of the largest social media platforms that allows users to post short comments (i.e., tweets). The unrestricted access to opinions and a large user base makes Twitter a major source for collection and quick dissemination of some health information. Health organizations, individuals, news organizations, businesses, and a host of other entities discuss health issues on Twitter. However, the enormous number of tweets presents challenges to those who seek to improve their knowledge of health issues. For instance, it is difficult to understand the overall sentiment on a health issue or the central message of the discourse. For Twitter to be an effective tool for health promotion, stakeholders need to be able to understand, analyze, and appraise health information and discussions on this platform. The purpose of this paper is to examine how a visual analytic study can provide insight into a variety of health issues on Twitter. Visual analytics enhances the understanding of data by combining computational models with interactive visualizations. Our study demonstrates how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on Twitter. In this paper, we report on the process of data collection, analysis of data, and representation of results. We present our findings and discuss the implications of this work to support the use of Twitter for health promotion.
社交媒体使得人们能够在传统健康领域之外探索有关健康问题的在线讨论。推特是最大的社交媒体平台之一,用户可以在上面发布简短评论(即推文)。对观点的无限制获取以及庞大的用户群体使推特成为收集和快速传播某些健康信息的主要来源。卫生组织、个人、新闻机构、企业以及许多其他实体都在推特上讨论健康问题。然而,大量的推文给那些试图增进对健康问题了解的人带来了挑战。例如,很难理解关于某个健康问题的整体情绪或讨论的核心信息。为了使推特成为促进健康的有效工具,利益相关者需要能够理解、分析和评估该平台上的健康信息及讨论。本文的目的是研究可视化分析研究如何能够洞察推特上的各种健康问题。可视化分析通过将计算模型与交互式可视化相结合,增强了对数据的理解。我们的研究展示了机器学习技术和可视化如何用于分析和理解推特上关于健康问题的讨论。在本文中,我们报告了数据收集、数据分析和结果呈现的过程。我们展示了研究结果,并讨论了这项工作的意义,以支持将推特用于健康促进。