Disaster Med Public Health Prep. 2023 Jun 15:1-39. doi: 10.1017/dmp.2023.101. Online ahead of print.
ABSTRACT
The rapid onset of COVID-19 created a complex virtual collective consciousness. Misinformation and polarization were hallmarks of the pandemic in the United States, highlighting the importance of studying public opinion online. Humans express their thoughts and feelings more openly than ever before on social media, co-occurrence of multiple data sources becomes valuable for monitoring and understanding public sentimental preparedness and response to an event within our society. In this study, Twitter and Google Trends data were used as the co-occurrence data for the understanding of the dynamics of sentiment and interest during the COVID-19 pandemic in the United States from January 2020 to September 2021. Developmental trajectory analysis of Twitter sentiment was conducted using corpus linguistic techniques and word cloud mapping to reveal eight positive and negative sentiments and emotions. Machine learning algorithms were employed to implement the opinion mining how Twitter sentiment was related to Google Trends interest with historical COVID-19 public health data. The sentiment analysis went beyond polarity to detect specific feelings and emotions during the pandemic. The discoveries on the behaviors of emotions at each stage of the pandemic were presented from the emotion detection when associated with the historical COVID-19 data and Google Trends data.
PMID:37317615 | DOI:10.1017/dmp.2023.101
