The Interplay Between Loneliness, Social Networks and Social Interactions for Digital Wellbeing Interventions
This project investigates the multidimensional factors associated with loneliness experiences, as well as an individual’s sense of belonging within their social network; for the development of tailored wellbeing interventions.
This project considers the interplay of loneliness experiences, social interactions and social network embeddedness from three primary perspectives: 1. Understanding the structures of and dynamics within social networks in relation to the experienced loneliness levels; 2. Investigating the functioning of ongoing wellbeing interventions for loneliness relief based on sociodemographic and personal factors; and 3. Proposing strategies for designing tailored digital wellbeing interventions based on the user’s needs.
By investigating the factors associated with the longevity of successful befriending schemes, the project aims to understand the directions in which the approach to this type of wellbeing intervention can be tailored to cater to individual preferences, particularly for scenarios where the process of pairing the users with volunteers is automated.
Insights for the potential and limitations of using real-world data for tailored befriending schemes are also provided for a customised methodological approach to the development of data-driven loneliness intervention strategies.
Moreover, feedback about the ongoing interventions for loneliness relief sought directly from the service users offers an invaluable opportunity to capture the impact of individual differences in designing tailored wellbeing solutions.
Finally, the project aims to build a comprehensive profile of the lonely person, cross-sectionally mapping out trends and themes in order to understand how the specific individual background and preferences affect social network embeddedness, belonging, and loneliness experiences. This way, the project will contribute to the provision of guidelines for tailored digital wellbeing interventions which take into account the individual user’s needs and preferences.
This project is supervised by Dr. John Harvey, Prof. James Goulding and Dr. Liz Dowthwaite.
The methodology includes: measurement of loneliness levels among the participants; collection of social network data as well as demographic information; a series of interviews to gain an in-depth understanding of the participants’ social relationships and loneliness experiences; in addition to the investigation of factors driving successful wellbeing interventions.
The data analysis methods include machine learning models and survival analysis of the factors associated with loneliness, belonging and successful wellbeing interventions; social network analysis; and thematic analysis. The aim is to gain an understanding of the relationship between loneliness levels, belonging, and the individuals’ social network functioning; to provide tailored, innovative digital wellbeing interventions catering to the users’ individual needs.
TBA
Predicting befriending scheme success using machine learning and neodemographic information.
The study of loneliness and social networks has largely involved social media analysis for both social circle insights and loneliness predictions from language. While geospatial data have also been used to investigate loneliness, the approach has predominantly involved … [more]
Digital footprints as means of measuring loneliness experience and embeddedness in social networks for designing digital mental health interventions.
Despite existing research evidence for the negative influence of loneliness on people’s wellbeing, most studies focus on the experiences of older adults and the student population. Moreover, research concerning loneliness and digital footprints uses … [more]
