# Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter

arXiv: https://arxiv.org/abs/2305.11398 · HTML: https://mlsanity.com/paper/2305.11398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Statistically significant detection of semantic shifts using contextual word embeddings](https://mlsanity.com/paper/2104.03776/) (2021)
- [Examining the consumption of radical content on YouTube](https://mlsanity.com/paper/2011.12843/) (2020)
- [Is Saki #delicious? The Food Perception Gap on Instagram and Its Relation to Health](https://mlsanity.com/paper/1702.06318/) (2017)
- [Analyzing the Language of Food on Social Media](https://mlsanity.com/paper/1409.2195/) (2014)
- [The Lexicocalorimeter: Gauging public health through caloric input and output on social media](https://mlsanity.com/paper/1507.05098/) (2015)
- [Who Watches (and Shares) What on YouTube? And When? Using Twitter to Understand YouTube Viewership](https://mlsanity.com/paper/1312.4511/) (2013)
- [You Tweet What You Eat: Studying Food Consumption Through Twitter](https://mlsanity.com/paper/1412.4361/) (2014)
- [#FoodPorn: Obesity Patterns in Culinary Interactions](https://mlsanity.com/paper/1503.01546/) (2015)
- [Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media](https://mlsanity.com/paper/1603.02718/) (2016)
- [Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter](https://mlsanity.com/paper/1709.07916/) (2017)
- [Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning](https://mlsanity.com/paper/1909.07881/) (2019)
- [YouTubing at Home: Media Sharing Behavior Change as Proxy for MobilityAround COVID-19 Lockdowns](https://mlsanity.com/paper/2103.14601/) (2021)

## Built on by (most cited first, 1 of 1)

- [Community Needs and Assets: A Computational Analysis of Community Conversations](https://mlsanity.com/paper/2403.13272/) (2024)

## Abstract (arXiv)

Unprecedented lockdowns at the start of the COVID-19 pandemic have drastically changed the routines of millions of people, potentially impacting important health-related behaviors. In this study, we use YouTube videos embedded in tweets about diet, exercise and fitness posted before and during COVID-19 to investigate the influence of the pandemic lockdowns on diet and nutrition. In particular, we examine the nutritional profile of the foods mentioned in the transcript, description and title of each video in terms of six macronutrients (protein, energy, fat, sodium, sugar, and saturated fat). These macronutrient values were further linked to demographics to assess if there are specific effects on those potentially having insufficient access to healthy sources of food. Interrupted time series analysis revealed a considerable shift in the aggregated macronutrient scores before and during COVID-19. In particular, whereas areas with lower incomes showed decrease in energy, fat, and saturated fat, those with higher percentage of African Americans showed an elevation in sodium. Word2Vec word similarities and odds ratio analysis suggested a shift from popular diets and lifestyle bloggers before the lockdowns to the interest in a variety of healthy foods, communal sharing of quick and easy recipes, as well as a new emphasis on comfort foods. To the best of our knowledge, this work is novel in terms of linking attention signals in tweets, content of videos, their nutrients profile, and aggregate demographics of the users. The insights made possible by this combination of resources are important for monitoring the secondary health effects of social distancing, and informing social programs designed to alleviate these effects.