Odlum Michelle, Cho Hwayoung, Broadwell Peter, Davis Nicole, Patrao Maria, Schauer Deborah, Bales Michael E, Alcantara Carmela, Yoon Sunmoo
School of Nursing, Columbia University, USA.
College of Nursing, University of Florida, USA.
Stud Health Technol Inform. 2020 Jun 26;272:24-27. doi: 10.3233/SHTI200484.
We randomly extracted publicly available Tweets mentioning COVID-19 related terms (n=2,558,474 Tweets) from Tweet corpora collected daily using an API from Jan 21st to May 3rd, 2020. We applied a clustering algorithm to publicly available Tweets authored by African Americans (n=1,763) to detect topics and sentiment applying natural language processing (NLP). We visualized fifteen topics (four themes) using network diagrams (Newman modularity 0.74). Compared to the COVID-19 related Tweets authored by others, positive sentiments, cohesively encouraging online discussions (e.g., Black strong 27.1%, growing up Blacks 22.8%, support Black business 17.0%, how to build resilience 7.8%), and COVID-19 prevention behaviors (e.g., masks 4.7%, encouraging social distancing 9.4%) were uniquely observed in African American Twitter communities. Application of topic modeling techniques to streaming social media Twitter provides the foundation for research team insights regarding information and future virtual based intervention and social media based health disparity research for COVID-19.
我们从2020年1月21日至5月3日使用应用程序编程接口（API）每日收集的推文语料库中随机提取了公开可用的提及与新冠病毒相关术语的推文（共2558474条推文）。我们对非裔美国人撰写的公开可用推文（1763条）应用了聚类算法，以通过自然语言处理（NLP）来检测主题和情感。我们使用网络图（纽曼模块化系数为0.74）直观展示了15个主题（4个主题类别）。与其他人撰写的与新冠病毒相关的推文相比，在非裔美国人的推特社区中独特地观察到了积极情绪，这些情绪凝聚性地鼓励了在线讨论（例如，黑人强大占27.1%，黑人成长占22.8%，支持黑人企业占17.0%，如何增强恢复力占7.8%）以及新冠病毒预防行为（例如，戴口罩占4.7%，鼓励保持社交距离占9.4%）。将主题建模技术应用于流式社交媒体推特，为研究团队提供了有关信息的见解，并为未来基于虚拟的干预措施以及基于社交媒体的新冠病毒健康差异研究奠定了基础。
