A unified ecological framework for studying effects of digital places on well-being

Social media has greatly expanded opportunities to study place and well-being through the availability of human expressions tagged with physical location. Such research often uses social media content to study how specific places in the offline world influence well-being without acknowledging that digital platforms (e.g., Twitter, Facebook, Youtube, Yelp) are designed in unique ways that structure certain types of interactions in online and offline worlds, which can influence place-making and well-being. To expand our understanding of the mechanisms that influence social media expressions about well-being, we describe an ecological framework of person-place interactions that asks, “at what broad levels of interaction with digital platforms and physical environments do effects on well-being manifest?” The person is at the centre of the ecological framework to recognize how people define and organize both digital and physical communities and interactions. The relevance of interactions in physical environments depends on the built and natural characteristics encountered across modes of activity (e.g., domestic, work, study). Here, social interactions are stratified into the meso-social (e.g., local social norms) and micro-social (e.g., personal conversations) levels. The relevance of interactions in digital platforms is contingent on specific hardware and software elements. Social interactions at the meso-social level include platform norms and passive use of social media, such as observing the expressions of others, whereas interactions at the micro-level include more active uses, like direct messaging. Digital platforms are accessed in a physical location, and physical locations are partly experienced through online interactions; therefore, interactions between these environments are also acknowledged. We conclude by discussing the strengths and limitations of applying the framework to studies of place and well-being

Personal activity centres and geosocial data analysis: Combining big data with small data

Understanding how people move and interact within urban settings has been greatly facilitated by the expansion of personal computing and mobile studies. Geosocial data derived from social media applications have the potential to both document how large segments of urban populations move about and use space, as well as how they interact with their environments. In this paper we examine spatial and temporal clustering of individuals’ geosocial messages as a way to derive personal activity centres for a subset of Twitter users in the City of Toronto. We compare the two types of clustering, and for a subset of users, compare to actual self-reported activity centres. Our analysis reveals that home locations were detected within 500 m for up to 53% of users using simple spatial clustering methods based on a sample of 16 users. Work locations were detected within 500 m for 33% of users. Additionally, we find that the broader pattern of geosocial footprints indicated that 35% of users have only one activity centre, 30% have two activity centres, and 14% have three activity centres. Tweets about environment were more likely sent from locations other than work and home, and when not directed to another user. These findings indicate activity centres defined from Twitter do relate to general spatial activities, but the limited degree of spatial variability on an individual level limits the applications of geosocial footprints for more detailed analyses of movement patterns in the city

Stresscapes: validating linkages between place and stress expression on social media

Understanding how individuals and groups perceive their surroundings and how different physical and social environments may influence their state-of-mind has intrigued re-searchers for some time. Much of this research has focused on investigating why certain natural and human-built places can engender specific emotive responses (e.g. fear, disgust, joy, etc.) and, by extension, how these responses can be considered in placemaking activities such as urban planning and design. Developing a better understanding of the linkages between place and emotional state is challenging in part because both cognitive processes and the concept of place are complex, dynamic and multi-faceted and are mediated by a confluence of contextual, individual and social processes. There is evidence to suggest that social media data produced by individuals in situ and in near real-time may provide novel insights into the nature and dynamics of individuals’ responses to their surroundings. The explosion of user-generated digital data and the sensorization of environments, especially in urban settings, provide opportunities to build knowledge of place and state-of-mind linkages that will inform the design and promotion of vibrant placemaking by individuals and communities. In this paper we present a novel study, to be undertaken this summer within the Greater Toronto area in Canada, with 140 recruited participants who are frequent, geo-tagging, Twitter users. The goal of the study will be to assess emotional, acute and chronic stress experienced in urban built-environments and as expressed during daily activities. An existing automated semantic natural language processing tool will be validated through this study, and it is hoped that the methodology developed can be extrapolated to other urban environments as well, with a second validation study already planned to take place next year in London, United Kingdom

Keywords used to search for AI-related Tweets

<p>Keywords used to search for AI-related Tweets</p

Official OIE outbreaks vs outbreaks identified on Twitter, blue vertical lines indicates dynamic method, green static method.

<p>Official OIE outbreaks vs outbreaks identified on Twitter, blue vertical lines indicates dynamic method, green static method.</p

Data cleaning operations performed on Tweets prior to Latent DIrichlet Allocation topic modelling

<p>Data cleaning operations performed on Tweets prior to Latent DIrichlet Allocation topic modelling</p

Wordclouds generated from original Tweets associated with topic models discovered from outbreaks based on the static threshold method (only 16 topics of 31 shown here).

<p>Wordclouds generated from original Tweets associated with topic models discovered from outbreaks based on the static threshold method (only 16 topics of 31 shown here).</p

Top ranking locations based on profile of account for static and dynamic outbreak AI-related Tweets.

<p>Top ranking locations based on profile of account for static and dynamic outbreak AI-related Tweets.</p

Time series of AI reports provided to the OIE during the study period.

<p>Time series of AI reports provided to the OIE during the study period.</p

Wordclouds generated from original Tweets associated with topic models discovered from outbreaks based on the dynamic threshold method.

<p>Wordclouds generated from original Tweets associated with topic models discovered from outbreaks based on the dynamic threshold method.</p