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The value of Twitter data for determining the emotional responses of people to urban green spaces: A case study and critical evaluation', Urban Studies.

To understand how the benefits of outdoor physical activity in urban green spaces are transferred to human populations, consideration must be given to when people are using them, what they are using them for and what factors may affect the use of space. This paper critically evaluates the use of crowdsourced Twitter data in an assessment of physical activity engagement in urban green spaces in an attempt to investigate the potential of these data in investigating urban socio-ecological interactions. A case study is presented in which Twitter data are used to assess the variance of physical activity engagement between two seasons (summer and winter). A number of factors including meteorology, park characteristics and amenities, and the role of organised sports events are explored in order to explain the observed findings. Understanding how physical activity engagement in urban green space varies seasonally is important in ensuring policy interventions to increase physical activity are targeted most effectively.

In urban research, Twitter data have the potential to provide additional information about urban citizens, their activities, mobility patterns and emotion. Extracting the sentiment present in tweets is increasingly recognised as a valuable approach to gathering information on the mood, opinion and emotional responses of individuals in a variety of contexts. This article evaluates the potential of deriving emotional responses of individuals while they experience and interact with urban green space. A corpus of over 10,000 tweets relating to 60 urban green spaces in Birmingham, United Kingdom was analysed for positivity, negativity and specific emotions, using manual, semi-automated and automated methods of sentiment analysis and the outputs of each method compared. Similar numbers of tweets were annotated as positive/neutral/negative by all three methods; however, inter-method consistency in tweet assignment between the methods was low. A comparison of all three methods on the same corpus of tweets, using character emojis as an additional quality control, identifies a number of limitations associated with each approach. The results presented have implications for urban planners in terms of the choices available to identify and analyse the sentiment present in tweets, and the importance of choosing the most appropriate method. Future attempts to develop more reliable and accurate algorithms of sentiment analysis are needed and should focus on semi-automated methods.

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