Food Access in New York City During the COVID-19 Pandemic: Social Media Monitoring Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic exacerbated issues of poverty and food insecurity in New York City, and many residents experienced difficulty accessing available resources to help them get food on the table. Social media presents an opportunity to observe and understand the barriers people face in accessing affordable, nutritious, and culturally appropriate foods. OBJECTIVE: This study aims to explore the food access discourse during the COVID-19 pandemic on Twitter (subsequently rebranded as X) in New York City by analyzing publicly available tweets posted from March 1, 2020, to March 31, 2021. METHODS: Tweets posted by individuals in New York City during the first 13 months of the COVID-19 pandemic were collected using the observation platform Talkwalker. We categorized a list of multiple keywords into related groups (search strings). Data were cleaned to keep only tweets relevant to food insecurity and food access in New York City and remove duplicate tweets. The software Botometer was used to remove accounts considered to be bots. Topic modeling was used to group these tweets into relevant themes, which were analyzed. The top viral tweets (ie, tweets that received the highest number of retweets in New York City) from this period were further analyzed. RESULTS: We identified 6 major themes (with subthemes) that emerged from the analysis (in order of popularity): community efforts, public assistance programs, grocery shopping and food workers, school foods, millions go hungry, and food justice. Interesting terms that emerged from the data were also identified. Overall, quantities of tweets increased in correlation with current events, such as the closure of New York City public schools; the expansion of the Supplemental Nutrition Assistance Program and unemployment benefits; the proliferation of mutual aid groups in the spring of 2020; and the May Day Instacart, Amazon, and Target strike in 2020. CONCLUSIONS: Findings revealed that in the earliest months of the COVID-19 pandemic, Twitter users in New York City quickly responded to the wave of need by sharing information and resources about food access in their communities. Some users turned to Twitter to either solicit or offer help finding food. Furthermore, the platform lent itself to many conversations about the policies enacted on a federal, state, and city level to help feed New Yorkers in need. Future research on this topic should include an analysis of social media posting on platforms such as Facebook, as well as languages other than English. Results from this type of research can provide information to community leaders and elected officials to better address future crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".