The Oxford English Dictionary defines stress as “a state of mental or emotional strain or tension resulting from adverse or demanding circumstances”. Broadly speaking, stress can be seen as a process in which demands put strain on an individual’s ability to adapt – physiologically and emotionally. Low levels of stress are key to our wellbeing, and according to prior research evidence, intensive acute and chronic psychological stress appears to play a causal role in the onset of multiple chronic disease outcomes, such as asthma and obesity, engendering notable costs related to economic productivity and health and social service spending. In an ongoing three-year study funded by the SSHRC (Social Sciences and Humanities Research Council, Canada), we have been focusing on researching links between experienced acute and chronic stress and related expressions of stress on geo-located social media streams, working together with academic partners from across the fields of geography and environmental studies, urban planning, as well as psychology at Canadian universities, including Wilfrid Laurier University, University of Waterloo and the University of Ottawa. This project involves the development of an entirely new ontology model, which builds on and extends the highly successful EMOTIVE system (see Sykora et al. 2013), for representing expressions of emotional stress. The basic idea is to evaluate and apply this model over large geo-located social media datasets (ie several millions of tweets) collated from high-density urban areas, as well as validating the approach on a small sample of Twitter users. The ontology model, which has been developed by Dr Suzanne Elayan, leverages various links between certain emotions that are often accompanied by stress; such as hopelessness, which often co-occurs with
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anxiety and aggressive behaviour, and a variety of feelings that coincide with stress. The model distinguishes between various semantics of the types of possible stress expressions and also provides a score of stress activation or intensity. A significant element of this study has been the ongoing recruitment of a sample of around 140 participants with Twitter-triggered daily surveys on their experiences of acute stress, which we are using to generate ‘Stresscapes’ to help us better understand personal activity centres at a geographical level, as well as exploring applications in urban planning and community stress within urban environments. Additionally we also conduct a collection of hair samples for cortisol analysis (ie as a biomarker for stress) in order to examine how our devised measure of stress predicts chronic activation and allostatic load (ie physiological dysfunction). Participants who agreed to this provided a 0.95 cm hair sample (from the root) at study exit, which will be analysed using immunoassay analysis following a validated protocol. Due to hair growing at a rate of approximately 1.25 cm per month, cortisol embedded in this sample length will reflect a retrospective record of approximately three prior weeks. Hair cortisol levels will be considered an outcome in regression models from our measure of stress. Ultimately we are aiming to improve our understanding of how individuals and groups perceive their surroundings from the analysis of social media and how different physical and social environments may influence their state of mind. A body of evidence suggests that the built environment shapes how we experience and respond to stress. However, there is a critical gap in our understanding of how our environments shape our experience of stressors and influence
how we cope with our perceived stress because of the availability (or lack thereof) of resources, eg a safe parking space. There is a lack of place-based measures of stress to facilitate research on these interrelationships. Our research within this project is certainly stimulating, but is only a step in this direction.
RELATED WORK ON MENTAL HEALTH AND PUBLIC HEALTH SURVEILLANCE According to the World Health Organisation, common mental disorders such as depression, bipolar affective disorder, dementia and schizophrenia affect about 410 million people globally, among which depression alone affects about 350 million people, making it the world’s fourth largest disease. Mental disorders can sometimes lead to self-harm, even suicide, which is a leading cause of death among teenagers and adults under 34 years of age. Given the pervasiveness of social media within younger demographics especially, we are also exploring statistical machine-learning models for predicting mental health conditions based on emotional, behavioural, linguistic and word choice profiles of Twitter users. Initial results of this work indicate that mental health conditions can be anticipated from such social media data, and that some can be predicted more accurately when emotional features from systems such as EMOTIVE are included, which is an exciting and interesting initial finding. Also, developing our semantically driven computational big data analysis of emotions further has resulted in a fruitful and ongoing collaboration with colleagues 11
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