Social Media Opinions on Working From Home in the United States During the COVID-19 Pandemic: Observational Study
Notice bibliographique
Résumé
BACKGROUND: Since March 2020, companies nationwide have started work from home (WFH) owing to the rapid increase of confirmed COVID-19 cases in an attempt to help prevent the disease from spreading and to rescue the economy from the pandemic. Many organizations have conducted surveys to understand people's opinions toward WFH. However, the findings are limited owing to a small sample size and the dynamic topics over time. OBJECTIVE: This study aims to understand public opinions regarding WFH in the United States during the COVID-19 pandemic. METHODS: We conducted a large-scale social media study using Twitter data to portray different groups of individuals who have positive or negative opinions on WFH. We performed an ordinary least squares regression analysis to investigate the relationship between the sentiment about WFH and user characteristics including gender, age, ethnicity, median household income, and population density. To better understand the public opinion, we used latent Dirichlet allocation to extract topics and investigate how tweet contents are related to people's attitude. RESULTS: On performing ordinary least squares regression analysis using a large-scale data set of publicly available Twitter posts (n=28,579) regarding WFH during April 10-22, 2020, we found that the sentiment on WFH varies across user characteristics. In particular, women tend to be more positive about WFH (P<.001). People in their 40s are more positive toward WFH than those in other age groups (P<.001). People from high-income areas are more likely to have positive opinions about WFH (P<.001). These nuanced differences are supported by a more fine-grained topic analysis. At a higher level, we found that the most negative sentiment about WFH roughly corresponds to the discussion on government policy. However, people express a more positive sentiment when discussing topics on "remote work or study" and "encouragement." Furthermore, topic distributions vary across different user groups. Women pay more attention to family activities than men (P<.05). Older people talk more about work and express a more positive sentiment regarding WFH. CONCLUSIONS: This paper presents a large-scale social media-based study to understand the public opinion on WFH in the United States during the COVID-19 pandemic. We hope that this study can contribute to policymaking both at the national and institution or company levels to improve the overall population's experience with WFH.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».