Background - During the ongoing COVID-19 pandemic, the immediate threat of illness and mortality is not the only concern. In the United States, COVID-19 is not only causing physical suffering to patients, but also great levels of adverse sentiment (e.g., fear, panic, anxiety) among the public. Such secondary threats can be anticipated and explained through sentiment analysis of social media, such as Twitter. Methods - We obtained a dataset of geotagged tweets on the topic of COVID-19 in the contiguous United States during the period of 11/1/2019 - 9/15/2020. We classified each tweet into "adverse" and "non-adverse" using the NRC Emotion Lexicon and tallied up the counts for each category per county per day. We utilized the space-time scan statistic to find clusters and a three-stage regression approach to identify socioeconomic and demographic correlates of adverse sentiment. Results - We identified substantial spatiotemporal variation in adverse sentiment in our study area/period. After an initial period of low-level adverse sentiment (11/1/2019 - 1/15/2020), we observed a steep increase and subsequent fluctuation at a higher level (1/16/2020 - 9/15/2020). The number of daily tweets was low initially (11/1/2019 - 1/22/2020), followed by spikes and subsequent decreases until the end of the study period. The space-time scan statistic identified 12 clusters of adverse sentiment of varying size, location, and strength. Clusters were generally active during the time period of late March to May/June 2020. Increased adverse sentiment was associated with decreased racial/ethnic heterogeneity, decreased rurality, higher vulnerability in terms of minority status and language, and housing type and transportation. Conclusions - We utilized a dataset of geotagged tweets to identify the spatiotemporal patterns and the spatial correlates of adverse population sentiment during the first two waves of the COVID-19 pandemic in the United States. The characteristics of areas with high adverse sentiment may be relevant for communication of containment measures. The combination of spatial clustering and regression can be beneficial for understanding of the ramifications of COVID-19, as well as disease outbreaks in general.
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