Diaz Marlon I, Hanna John J, Hughes Amy E, Lehmann Christoph U, Medford Richard J
Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
Open Forum Infect Dis. 2022 Jun 6;9(7):ofac263. doi: 10.1093/ofid/ofac263. eCollection 2022 Jul.
We explore the ivermectin discourse and sentiment in the United States with a special focus on political leaning through the social media blogging site Twitter.
We used sentiment analysis and topic modeling to geospatially explore ivermectin Twitter discourse in the United States and compared it to the political leaning of a state based on the 2020 presidential election.
All modeled topics were associated with a negative sentiment. Tweets originating from democratic leaning states were more likely to be negative.
Real-time analysis of social media content can identify public health concerns and guide timely public health interventions tackling disinformation.
我们通过社交媒体博客网站推特，特别关注政治倾向，探讨美国的伊维菌素相关言论和情绪。
我们使用情感分析和主题建模，对美国伊维菌素相关推特言论进行地理空间探索，并将其与基于2020年总统选举的州政治倾向进行比较。
所有建模主题都与负面情绪相关。来自倾向民主党的州的推文更有可能是负面的。
对社交媒体内容的实时分析可以识别公共卫生问题，并指导及时应对虚假信息的公共卫生干预措施。
