Food Access in New York City During the COVID-19 Pandemic: Social Media Monitoring Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».