Food insufficiency and Twitter emotions during a pandemic

Stephan J. Goetz, Connor Heaton, Muhammad Imran, Yuxuan Pan, Zheng Tian, Claudia Schmidt, Umair Qazi, Ferda Ofli, Prasenjit Mitra

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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 joy dominated in these tweets nationally, but only anger, disgust, and fear 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.

Original languageEnglish (US)
Pages (from-to)1189-1210
Number of pages22
JournalApplied Economic Perspectives and Policy
Volume45
Issue number2
DOIs
StatePublished - Jun 2023

All Science Journal Classification (ASJC) codes

  • Development
  • Economics and Econometrics

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