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Understanding Sentiments and Activities in Green Spaces using a Social Data-driven Approach

Lim, KH, Lee, KE, Kendal, D ORCID: 0000-0003-2816-1722, Rashidi, L, Naghizade, E, Feng, Y and Wang, J 2019 , 'Understanding Sentiments and Activities in Green Spaces using a Social Data-driven Approach', in M Lytras A Visvizi (ed.), Smart Cities: Issues and Challenges - Mapping Political, Social and Economic Risks and Threats , Elsevier, United States, pp. 77-107.

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Abstract

Green spaces are believed to enhance the well-being of residents in urban areas. While there is research exploring the emotional benefits of green spaces, most early works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments, emotions and activities in Melbourne, Australia and our main findings are: (i) tweets in green spaces contain more positive and less negative emotions compared to those in urban areas; (ii) emotions in tweets vary seasonally; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks; and (v) each green space is often associated with a specific type of activity and/or event, which can be a useful information for Online recommender systems. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which builds upon traditional methods in urban research and provides important implications for urban planning authorities.

Item Type: Book Section
Authors/Creators:Lim, KH and Lee, KE and Kendal, D and Rashidi, L and Naghizade, E and Feng, Y and Wang, J
Keywords: green space, urban, Twitter, sentiment analysis, topic modelling, activity recommendation
Publisher: Elsevier
DOI / ID Number: 10.1016/B978-0-12-816639-0.00006-5
Copyright Information:

Copyright 2019 Elsevier

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