Food insufficiency and Twitter emotions during a pandemic

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.

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

“…Forecasting ability is a potential factor affecting supply chain performance [ 27 - 29 ]. One study found that tweets related to food insecurity were strongly correlated with real food insufficiencies [ 16 ]; the authors noted that there is potential for tweet sentiment analysis to be used as a cost-effective early warning system that can help direct food-related interventions. Similarly, our results suggest that there is potential for negative sentiment COVID-19 posts to relate to actual medical resource shortage in regions where people use public discourse platforms such as Twitter (since rebranded as X).…”

“…Given that social media are an emerging source for real-time and easily accessible information, there is potential to leverage data from social media for forecasting real-world outcomes [ 12 - 15 ]. Social media platforms and web search data host a wealth of real-time data that broadly reflect the current state of affairs in a particular region [ 16 ]. Although the standards of validation for these new data streams are still being validated because they do not have a track record of use, these unconventional data sources have the potential to aid in short- and long-term surveillance, although the surveillance goals must be clearly defined.…”

Exploring food poverty experiences in the German Twitter-Sphere

“…Some social media-related studies on food insecurity or poverty have analysed food-related tweets and metadata to identify geographical dynamics or food deserts associated with inadequate access to healthy foods [54,55]. Other studies have focused on sentiment analysis to examine emotions or reactions expressed on Twitter, particularly in response to emergency food supplies [56]. From a discourse analytical perspective, some studies have explored the thematic complex of food insecurity and food poverty, investigating the framing of hunger in public discourse and its use as a political tool to assert moral claims on the state [57].…”

Investigating Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

“…Its vast pool of information not only serves to heighten public awareness but also acts as a beacon, illuminating the locations and contexts of outbreaks. This wealth of real-time data from Twitter proves invaluable in shedding light on the multifaceted aspects of a wide range of topics and matters of interest to the scientific community from different disciplines, such as infectious disease outbreaks [57][58][59][60][61], cryptocurrency and stock markets [62,63], public health concerns [64][65][66][67], societal problems [68][69][70][71][72], emerging technologies [73,74], human behavior analysis [75][76][77][78], and humanitarian issues [79][80][81][82][83], as can be seen from several prior works in these fields, which focused on sentiment analysis and other forms of content analysis of Tweets. Following the COVID-19 epidemic, a growing corpus of studies have used Twitter data to analyze public reactions during this global health emergency [84,85].…”