Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence

作者：Man Hung, Evelyn Lauren, Eric S. Hon, Wendy C. Birmingham, Julie Y. Xu, Sharon Su, Shirley D. Hon, Jungweon Park, Peter Dang, Martin Stephen Lipsky · 发表于：Journal of Medical Internet Research · 年份：2020 · DOI：10.2196/22590 · 被引用次数：259 · 研究领域：Misinformation and Its Impacts、Public Relations and Crisis Communication、Data-Driven Disease Surveillance

BACKGROUND: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. OBJECTIVE: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. METHODS: This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19-related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. RESULTS: There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19-related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative s...