Spatiotemporal variations of public opinion on social distancing in the Netherlands

Comparison of Twitter and longitudinal survey data

Dados Bibliográficos

ID22068115
AutoresChao Zhang (0000-0003-3686-4743, Utrecht University, autor correspondente), Shihan Wang (0000-0001-6854-9217, Utrecht University), Erik Tjong Kim Sang (0000-0002-8431-081X, Netherlands eScience Center), Marieke A Adriaanse (0000-0002-2218-4723, Utrecht University), Lidewij Tummers (0000-0001-9940-9874, Utrecht University), Lars Tummers, Marijn Schraagen (Utrecht University), Ji Qi (0000-0003-3324-5269, University of Amsterdam), Mehdi Dastani (0000-0002-4641-4087, Utrecht University), Henk Aarts (0000-0001-8676-1692, Utrecht University)
Ano2022
Volume10
Páginas856825-856825
Data de publicação2022-07-28
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2022.856825
PMID35968468
OpenAlexW4288445075
IdiomaEN
Referências citadas43

Background: Social distancing has been implemented by many countries to curb the COVID-19 pandemic. Understanding public support for this policy calls for effective and efficient methods of monitoring public opinion on social distancing. Twitter analysis has been suggested as a cheaper and faster-responding alternative to traditional survey methods. The current empirical evidence is mixed in terms of the correspondence between the two methods. Objective: We aim to compare the two methods in the context of monitoring the Dutch public's opinion on social distancing. For this comparison, we quantified the temporal and spatial variations in public opinion and their sensitivities to critical events using data from both Dutch Twitter users and respondents from a longitudinal survey. Methods: = 1,200) was conducted between July and November 2020 to measure opinions on social distancing weekly. From the same period, near 100,000 Dutch tweets were categorized as supporting or rejecting social distancing based on a model trained with annotated data. Average stances for the 12 Dutch provinces and over the 20 weeks were computed from the two data sources and were compared through visualizations and statistical analyses. Results: Both data sources suggested strong support for social distancing, but public opinion was much more varied among tweets than survey responses. Both data sources showed an increase in public support for social distancing over time, and a strong temporal correspondence between them was found for most of the provinces. In addition, the survey but not Twitter data revealed structured differences among the 12 provinces, while the two data sources did not correspond much spatially. Finally, stances estimated from tweets were more sensitive to critical events happened during the study period. Conclusions: Our findings indicate consistencies between Twitter data analysis and survey methods in describing the overall stance on social distancing and temporal trends. The lack of spatial correspondence may imply limitations in the data collections and calls for surveys with larger regional samples. For public health management, Twitter analysis can be used to complement survey methods, especially for capturing public's reactivities to critical events amid the current pandemic

Distancing · Geography · Political science · Politics · Public opinion · Social distance · Social media · Statistics · Survey data collection · World Wide Web · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Psychology · Survey Methodology and Nonresponse