The COVID-19 pandemic initially caused worldwide concerns about food insecurity. Tweets analyzed in real-time may help food assistance providers target food supplies to where they are most urgently needed. In this exploratory study, we use natural language processing to extract sentiments and emotions expressed in food security-related tweets early in the pandemic in U.S. states. The emotion dominated in these tweets nationally, but only , , and were also statistically correlated with contemporaneous food insufficiency rates reported in the Household Pulse Survey; more nuanced and statistically stronger correlations are detected within states, including a negative correlation with joy.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082005PMC
http://dx.doi.org/10.1002/aepp.13258DOI Listing

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