TY - JOUR
T1 - Spatiotemporal variations of public opinion on social distancing in the Netherlands
T2 - Comparison of Twitter and longitudinal survey data
AU - Zhang, Chao
AU - Wang, Shihan
AU - Tjong Kim Sang, Erik
AU - Adriaanse, Marieke A
AU - Tummers, Lars
AU - Schraagen, Marijn
AU - Qi, Ji
AU - Dastani, Mehdi
AU - Aarts, Henk
N1 - Funding Information:
The Twitter data collection and analysis work was funded by Netherlands eScience Center under the project PuReGoMe (27020S04). We thank Nina Breedveld for helping with the annotation of Twitter data. The longitudinal survey study was supported by a grant awarded to HA by the Faculty of Social and Behavioral Sciences at Utrecht University for studying habits in the COVID-19 context and by the Alliance project HUMAN-AI funded by Utrecht University, Eindhoven University of Technology, Wageningen University & Research, and University Medical Center Utrecht.
Publisher Copyright:
Copyright © 2022 Zhang, Wang, Tjong Kim Sang, Adriaanse, Tummers, Schraagen, Qi, Dastani and Aarts.
PY - 2022/7/28
Y1 - 2022/7/28
N2 - 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: A longitudinal survey on a representative Dutch sample (n = 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.
AB - 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: A longitudinal survey on a representative Dutch sample (n = 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.
KW - COVID-19
KW - longitudinal survey
KW - public opinion
KW - social distancing
KW - social media data
KW - spatiotemporal analysis
KW - stance analysis
UR - http://www.scopus.com/inward/record.url?scp=85135834594&partnerID=8YFLogxK
U2 - 10.3389/fpubh.2022.856825
DO - 10.3389/fpubh.2022.856825
M3 - Article
C2 - 35968468
SN - 2296-2565
VL - 10
SP - 1
EP - 14
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 856825
ER -