Leveraging Social Media Data to Understand the Impact of COVID-19 on Residents' Dietary Behaviors: Observational Study.

Background: The COVID-19 pandemic has inflicted global devastation, infecting over 750 million and causing 6 million deaths. In an effort to control the spread of the virus, governments around the world implemented a variety of measures, including stay-at-home orders, school closures, and mask mandates. These measures had a substantial impact on dietary behavior, with individuals discussing more home-cooked meals and snacking on social media.

Objective: The study explores pandemic-induced dietary behavior changes using Twitter images and text, particularly in relation to obesity, to inform interventions and understand societal influences on eating habits. Additionally, the study investigates the impact of COVID-19 on emotions and eating patterns.

Methods: In this study, we collected approximately 200,000 tweets related to food between May and July in 2019, 2020, and 2021. We used transfer learning and a pretrained ResNet-101 neural network to classify images into 4 health categories: definitely healthy, healthy, unhealthy, and definitely unhealthy. We then used the state obesity rates from the Behavioral Risk Factor Surveillance System (BRFSS) to assess the correlation between state obesity rates and dietary images on Twitter. The study further investigates the effects of COVID-19 on emotional changes and their relation to eating patterns via sentiment analysis. Furthermore, we illustrated how the popularity of meal terms and health categories changed over time, considering varying time zones by incorporating geolocation data.

Results: A significant correlation was observed between state obesity rates and the percentages of definitely healthy (r=-0.360, P=.01) and definitely unhealthy (r=0.306, P=.03) food images in 2019. However, no trend was observed in 2020 and 2021, despite higher obesity rates. A significant (P<.001) increase in the percentage of healthy food consumption was observed during (39.99% in 2020) and after the shutdown (39.32% in 2021), as compared with the preshutdown period (37.69% in 2019). Sentiment analysis from 2019, 2020, and 2021 revealed a more positive sentiment associated with dietary posts from 2019. This was the case regardless of the healthiness of the food mentioned in the tweet. Last, we found a shift in consumption time and an increase in snack consumption during and after the pandemic. People ate breakfast later (ie, from 7 AM to 8 AM in 2019 to 8 AM to 9 AM in 2020 and 2021) and dinner earlier (ie, from 6 PM to 7 PM in 2019, to 5 PM to 6 PM in 2020). Snacking frequency also increased. Taken together, dietary behavior shifted toward healthier choices at the population level during and after the COVID-19 shutdown, with potential for long-term health consequences.

Conclusions: We were able to observe people's eating habits using social media data to investigate the effects of COVID-19 on dietary behaviors. Deep learning for image classification and text analysis was applied, revealing a decline in users' emotions and a change in dietary patterns and attitudes during and after the lockdown period. The findings of this study suggest the need for further investigations into the factors that influence dietary behaviors and the pandemic's implications of these changes for long-term health outcomes.