Measuring policy debate in a regrowing city by sentiment analysis using online media data: A case study of Leipzig 2030

Abstract

In a time of data science, online crowdsourced data and advanced data analytics would be a waste of resources and knowledge if these tools wouldn't assist urban planning. This paper uses sentiment analysis to understand policy debates' present in online media in a re-urbanized city and discusses how these tools and new datasets can help planners and inform new waves of policy by including groups that tend to be underrepresented in traditional consultation meetings. Modern reurbanization is an emergent phenomenon that occurs in cities that experienced urban shrinkage in the past decades. Urban policies proposed to the "new" regrowing cities should meet challenges of old shrinking consequences and new regrowing demands. Nowadays, policy-makers need to hear the voice from different age groups, some use new, non-traditional media in policy debates. Online media data has the potential to provide quasi-live feedbacks, to supply the traditional opinion surveys. We applied sentiment analysis in the selected casethe Integrated Urban Development Concept (INSEK) Leipzig 2030to evaluate public opinion. While results show that public attention was low, the policy seems to be welcome, with the public showing more interests in regrowth topics than shrinkage issues.

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“…Public sentiment can also be influential by region or class, such as investor sentiment influencing prices, volumes and volatility in stock markets [11], [60]. Sentiment analysis has been measured using tweets features such as hashtags, and it has been used for design policy and education policy as well [61]- [63]. Given the breadth and depth of the influence of public sentiment, it is an important factor to study to better understand the emerging post-COVID-19 new normal scenarios.…”

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“…Before the COVID-19 pandemic, real-time information from social media can support emergency management in public engagement, public-private communication, situation monitoring, social cohesion creation, intervention evaluation, and collaborative governance ( Alexander, 2014 ; Gibbons, Nara, & Appleyard, 2018 ; Pereira, Parycek, Falco, & Kleinhans, 2018 ; Vayansky, Kumar, & Li, 2019 ; Xu et al, 2020 ). During the COVID-19 pandemic, social media data have been used in understanding government responses because it is one of the fastest ways to sense governmental communication strategies, political preference, public attention and sentiment ( Ahmed, Rabin, & Chowdhury, 2020 ; Chen, Silva, & Reis, 2021 ; Shen et al, 2020 ; Tsao et al, 2021 ; Wu et al, 2022 ).…”

“…Sentiment analysis can be used to reveal the positive, negative or neutral tones of textual user-generated content ( Nielsen, 2011 ). In order to explore public opinion towards specific issues/policies, researchers such as Chen, Silva, and Reis (2021) and Vayansky et al (2019) conduct sentiment analyses to extract the sentiment polarity of the public. Topic modelling is another widely utilised approach in further investigating preeminent topics ( Abd-Alrazaq, Alhuwail, Househ, Hamdi, & Shah, 2020 ).…”

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“…SA research has focused on public opinion about rural issues as well-issues pertaining to a section of the population that has limited to the internet where these issues are discussed, which is ironic to think about (Vagrani et al, 2020;Botterill, 2009;Singh et al, 2018Singh et al, , 2019Li et al, 2021). Social or political campaigns run by the governments/politicians are also topics of discussion amongst the online population which can help predict the public opinion towards a campaign (Tayal and Yadav, 2017;Sandoval-Almazan and Valle-Cruz, 2020;Hoffmann, 2018;Budiharto and Meiliana, 2018;Chen et al, 2021). The analysis of online text or opinion can help create a positive impact on the policy as governments can be better informed about public opinion.…”