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Food insufficiency and Twitter emotions during a pandemic

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/aepp.13069openalex-snapshot

Measuring Food Insecurity during the COVID‐19 Pandemic of Spring 2020

inferredcites →https://doi.org/10.1002/aepp.13096openalex-snapshot

Emergency Food Provision for Children and Families during the COVID ‐19 Pandemic: Examples from Five U.S. Cities

inferredcites →https://doi.org/10.1002/aepp.13099openalex-snapshot

Food Hardship during the COVID‐19 Pandemic and Great Recession

inferredcites →https://doi.org/10.1002/aepp.13100openalex-snapshot

Food Insecurity during COVID ‐19

inferredcites →https://doi.org/10.1002/aepp.13105openalex-snapshot

Potential Impacts of a Pandemic on the US Farm Labor Market

inferredcites →https://doi.org/10.1002/aepp.13107openalex-snapshot

The Effects of COVID ‐19 on Fruit and Vegetable Production

inferredcites →https://doi.org/10.1002/aepp.13128openalex-snapshot

Unprecedented Crisis Calls for Unprecedented Policy Responses

inferredcites →https://doi.org/10.1002/aepp.13214openalex-snapshot

Machine learning for food security: Principles for transparency and usability

inferredcites →https://doi.org/10.1002/aepp.13258openalex-snapshot

Food insufficiency and Twitter emotions during a pandemic

inferredcites →https://doi.org/10.1002/ajae.12017openalex-snapshot

Relevant and/or Elegant Economics

inferredcites →https://doi.org/10.1002/asi.21416openalex-snapshot

Sentiment strength detection in short informal text

inferredcites →https://doi.org/10.1007/978-981-10-5209-5openalex-snapshot

Deep Learning in Natural Language Processing

inferredcites →https://doi.org/10.1007/s10479-016-2303-4openalex-snapshot

Use of twitter data for waste minimisation in beef supply chain

inferredcites →https://doi.org/10.1007/s12525-016-0228-zopenalex-snapshot

Predicting user behavior in electronic markets based on personality-mining in large online social networks

inferredcites →https://doi.org/10.1016/j.ajp.2020.102089openalex-snapshot

Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India

inferredcites →https://doi.org/10.1016/j.jbusres.2020.05.035openalex-snapshot

Covid-19′s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data

inferredcites →https://doi.org/10.1016/j.psj.2020.07.011openalex-snapshot

#Eggs: social and online media-derived perceptions of egg-laying hen housing

inferredcites →https://doi.org/10.1016/j.tre.2017.05.008openalex-snapshot

Social media data analytics to improve supply chain management in food industries

inferredcites →https://doi.org/10.1016/s2095-3119(17)61859-8openalex-snapshot

Agricultural remote sensing big data: Management and applications

inferredcites →https://doi.org/10.1038/s43016-020-0085-yopenalex-snapshot

Actions now can curb food systems fallout from COVID-19

inferredcites →https://doi.org/10.1080/02699939208411068openalex-snapshot

An argument for basic emotions

inferredcites →https://doi.org/10.1093/aepp/ppx056openalex-snapshot

Big Data in Agriculture: A Challenge for the Future

inferredcites →https://doi.org/10.1126/science.1243089openalex-snapshot

Economics in the age of big data

inferredcites →https://doi.org/10.1126/science.abc4765openalex-snapshot

COVID-19 risks to global food security

inferredcites →https://doi.org/10.1145/3340531.3412765openalex-snapshot

TweetsCOV19 - A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

inferredcites →https://doi.org/10.1145/3404111.3404114openalex-snapshot

GeoCoV19

inferredcites →https://doi.org/10.1177/2053951716648174openalex-snapshot

Big Data in food and agriculture

inferredcites →https://doi.org/10.18653/v1/2020.acl-main.372openalex-snapshot

GoEmotions: A Dataset of Fine-Grained Emotions

inferredcites →https://doi.org/10.18653/v1/d13-1170openalex-snapshot

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

inferredcites →https://doi.org/10.18653/v1/s17-2088openalex-snapshot

SemEval-2017 Task 4: Sentiment Analysis in Twitter

inferredcites →https://doi.org/10.18653/v1/s18-1038openalex-snapshot

CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons

inferredcites →https://doi.org/10.18653/v1/w19-4828openalex-snapshot

What Does BERT Look at? An Analysis of BERT’s Attention

inferredcites →https://doi.org/10.2196/19016openalex-snapshot

Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study

inferredcites →https://doi.org/10.2196/19447openalex-snapshot

Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends

inferredcites →https://doi.org/10.2200/s00762ed1v01y201703hlt037openalex-snapshot

Neural Network Methods for Natural Language Processing

inferredcites →https://doi.org/10.22004/ag.econ.313316openalex-snapshot

No title

inferredcites →https://doi.org/10.2466/pr0.1989.65.1.223openalex-snapshot

Note on Cohen's Kappa

inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot

AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

inferredcites →https://doi.org/10.4315/jfp-21-085openalex-snapshot

Online Media Attention Devoted to Flour and Flour-Related Food Safety in 2017 to 2020

inferredcites →https://doi.org/10.48550/arxiv.1809.06683openalex-snapshot

RumourEval 2019: Determining Rumour Veracity and Support for Rumours

inferredcites →https://doi.org/10.5304/jafscd.2020.093.033openalex-snapshot

Americans' Food Spending Patterns Explain Devastating Impact of COVID-19 Lockdowns on Agriculture

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4762168openalex-extracted

Learning Through the Social Conditions Experienced by Orphans During #Covid-19 with Social Media Mining

inferred← citeshttps://doi.org/10.1007/978-3-031-48044-7_27openalex-snapshot

Analysis of User Diversity-Based Patterns of Public Discourse on Twitter About Mental Health in the Context of Online Learning During COVID-19

inferred← citeshttps://doi.org/10.1007/s41870-024-02263-5openalex-snapshot

Improving nonprofit engagement on social media: Using big data, machine-learning, and sentiment analysis to evaluate leading nonprofits’ message strategies on Twitter/X

inferred← citeshttps://doi.org/10.1007/s43621-025-02209-2openalex-snapshot

The role of language technology and artificial intelligence in food security policymaking

inferred← citeshttps://doi.org/10.1016/j.diggeo.2024.100110openalex-snapshot

Characterizing climate change sentiments in Alaska on social media

inferred← citeshttps://doi.org/10.1016/j.jadohealth.2024.09.008openalex-snapshot

What Effect Did COVID-19 Have on Adolescent Food Insufficiency in Africa and Asia? Evidence From the Global Early Adolescent Study

inferred← citeshttps://doi.org/10.1108/ijsms-09-2024-0266openalex-snapshot

Beauty premium and penalty in sport: text analysis of Instagram comments in the Korean women’s volleyball league

inferred← citeshttps://doi.org/10.1109/bigdata59044.2023.10386346openalex-snapshot

HungerGist: An Interpretable Predictive Model for Food Insecurity

inferred← citeshttps://doi.org/10.1109/icst56971.2022.10136257openalex-snapshot

Hierarchical Neural Network Implementation: Emotion Recognition for Food Security Comments on Twitter

inferred← citeshttps://doi.org/10.1186/s12889-024-18926-8openalex-snapshot

Exploring food poverty experiences in the German Twitter-Sphere

inferred← citeshttps://doi.org/10.1371/journal.pone.0328210openalex-snapshot

Estimating affective polarization on a social network

inferred← citeshttps://doi.org/10.2196/46087openalex-snapshot

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

inferred← citeshttps://doi.org/10.32731/ijsf.202.052025.04openalex-snapshot

Reference Points, Loss Aversion, and Sport: Text Analysis of Football Highlight Comments

inferred← citeshttps://doi.org/10.3390/asi6050092openalex-snapshot

Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

inferred← citeshttps://doi.org/10.3390/bdcc7020116openalex-snapshot

Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox

inferred← citeshttps://doi.org/10.3390/computers12100191openalex-snapshot

Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets

inferred← citeshttps://doi.org/10.3390/fi15100346openalex-snapshot

A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023

inferred← citeshttps://doi.org/10.3390/foods14172965openalex-snapshot

Exploring Consumer Perception of Food Insecurity Using Big Data

inferred← citeshttps://doi.org/10.37625/abr.28.1.203-222openalex-snapshot

Emotion Analysis and Topic Modelling of Supply Chain Discussion during the COVID-19 Pandemic

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