A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
The road to recovery: Sensing public opinion towards reopening measures with social media data in post-lockdown cities
Abstract The COVID-19 pandemic has resulted in cities implementing lockdown measures, causing unprecedented disruption (e.g. school/shop/office closures) to urban life often extending over months. With the spread of COVID-19 now being relatively contained, many cities have started to ease their lockdown restrictions by phases. Following the phased recovery strategy proposed by the UK government following the first national lockdown, this paper utilises Greater London as its case study, selecting three main reopening measures (i.e., schools, shops and hospitality reopening). This paper applies sentiment analysis and topic modelling to explore public opinions expressed via Twitter. Our findings reveal that public attention towards the reopening measures reached a peak before the date of policy implementation. The attitudes expressed in discussing reopening measures changed from negative to positive. Regarding the discussed topics related to reopening measures, we find that citizens are more sensitive to early-stage reopening than later ones. This study provides a time-sensitive approach for local authorities and city managers to rapidly sense public opinion using real-time social media data. Governments and policymakers can make use of the framework of sensing public opinion presented herein and utilise it in leading their post-lockdown cities into an adaptive, inclusive and smart recovery.
Highlights Twitter data timely capture the public opinion regarding reopening policies. Sentiment analysis reveals public attitudes to reopening measures. Topic modelling was utilised for exploring topics about reopening. Urban citizens are more sensitive to the earlier lockdown lifting measures Public attention to announcements provides early feedback to implementation.
The road to recovery: Sensing public opinion towards reopening measures with social media data in post-lockdown cities
Abstract The COVID-19 pandemic has resulted in cities implementing lockdown measures, causing unprecedented disruption (e.g. school/shop/office closures) to urban life often extending over months. With the spread of COVID-19 now being relatively contained, many cities have started to ease their lockdown restrictions by phases. Following the phased recovery strategy proposed by the UK government following the first national lockdown, this paper utilises Greater London as its case study, selecting three main reopening measures (i.e., schools, shops and hospitality reopening). This paper applies sentiment analysis and topic modelling to explore public opinions expressed via Twitter. Our findings reveal that public attention towards the reopening measures reached a peak before the date of policy implementation. The attitudes expressed in discussing reopening measures changed from negative to positive. Regarding the discussed topics related to reopening measures, we find that citizens are more sensitive to early-stage reopening than later ones. This study provides a time-sensitive approach for local authorities and city managers to rapidly sense public opinion using real-time social media data. Governments and policymakers can make use of the framework of sensing public opinion presented herein and utilise it in leading their post-lockdown cities into an adaptive, inclusive and smart recovery.
Highlights Twitter data timely capture the public opinion regarding reopening policies. Sentiment analysis reveals public attitudes to reopening measures. Topic modelling was utilised for exploring topics about reopening. Urban citizens are more sensitive to the earlier lockdown lifting measures Public attention to announcements provides early feedback to implementation.
The road to recovery: Sensing public opinion towards reopening measures with social media data in post-lockdown cities
Chen, Yiqiao (author) / Niu, Haifeng (author) / Silva, Elisabete A. (author)
Cities ; 132
2022-10-19
Article (Journal)
Electronic Resource
English
Mining Public Opinion on Transportation Systems Based on Social Media Data
| DOAJ | 2019