COVID-19 pandemic and food poverty conversations: Social network analysis of Twitter data.

Abstract

This novel and mixed-method study investigated food poverty conversations at the beginning of the COVID-19 pandemic and the subsequent national lockdown on the social media platform Twitter. NodeXL Pro software was used to collect tweets using the terms 'food' and 'poverty' in any order somewhere in a tweet sent on selected days between April 5 and May 23, 2020. The data obtained from NodeXL Pro were cleaned. Social network analysis tools were used to analyse and visualise our data. Using this method, sentiment-related words (positive or negative words), the top (the most mentioned) 10 hashtags, top words and top word pairs were identified. The patterns of word pairs communicated in our network were visualised based on each word pair's frequency. This also enabled us to carry out a content analysis to create coding of the word pairs' data. A total of 81249 tweets were identified that contained the terms 'food' and 'poverty'. Our findings revealed that individuals' tweets overwhelmingly contained views about the increase in hunger, food poverty and food insecurity due to the COVID-19 pandemic. Twitter users perceived that when the pandemic measures began, many food-secure families were pushed into food insecurity due to a rapid rise in unemployment and rising poverty due to the quarantine and stay-at-home instructions in place at the time. They also addressed the sharp rise in food poverty being driven by panic buying, food shortages, food affordability and disruptions in food supply and food systems. Our analysis of this data suggests that to mitigate food poverty or to prevent a 'hunger pandemic' for future pandemic emergencies, comprehensive and longer term policy responses and economic supports are needed to strengthen the resilience of food systems. However, the highlighted limitations of this study must be considered.