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Introduction & Background 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 demographic proxies, as opposed to a behavioural focus, thus providing an incomplete representation of the phenomenon’s influence on the general population. Objectives & Approach This project aims to use people’s digital social media data (shared by the participants from their Facebook, Twitter, or Reddit accounts) to address people’s experiences of loneliness in order to provide guidance for the design of interventions catering to the improvement of the wellbeing of individuals. Screening the participants for loneliness levels using the UCLA Loneliness Scale and looking how these experiences differ cross-sectionally (25-65-year-olds; minorities) will help understand the following: social network structures, as shaped by loneliness experience; the dynamics within one’s social network; and the linguistic content of the relationships. Using digital footprints for language modelling and thematic analysis of digital language data shared by the participants, in addition to social network analysis (mapped out based on the individuals’ digital interactions) will allow insight into digital wellbeing. A traditional approach will be utilised alongside digital data analysis to address the issue of limited social media data representativeness - relationships formed in non-digital settings, along with the associated loneliness experiences, will be included. In addition to sharing their digital footprints, the participants will be surveyed and interviewed about their everyday offline and digital experiences of loneliness; as well as their social network structures and dynamics. The interview and survey data will be analysed using thematic analysis of text data and predictive models of quantitative survey responses; in addition to social network analysis of the relationships listed during the interview. Predictions of loneliness outcomes in relation to people’s digital and offline behaviour; and correlations between loneliness experiences and social network dynamics will be made from the data. Relevance to Digital Footprints The project focuses on people’s experiences of loneliness in both digital and offline settings utilising the analysis of digital footprints from social media and traditional survey- and interview-based methodology. This approach will allow to gain insight into the similarities and differences between social network structures and dynamics as well as loneliness experiences in digital and real-world relationships as these inevitably interplay in everyday life. The inclusion of digital footprints data will allow to measure and predict loneliness impact on mental health and digital social behaviour; and design tailored digital wellbeing interventions in the future. Conclusions & Implications The results will serve as basis for designing innovative loneliness interventions for both the public and counsellors to offer tailored wellbeing support. Additionally, the project aims to promote loneliness awareness. Incorporating a variety of experiences from different social groups will enable inclusive, user-centred approach.

Introduction & Background This work demonstrates the development of an Adult Session Wants and Needs Outcome Measure (Adult SWAN-OM) aimed at supporting service delivery within the digital mental health platform (DMHP), Qwell. Qwell is a DMHP commissioned by the United Kingdom’s National Health Service which provides access to an online community of peers, a team of experienced counsellors, and a cadre of emotional well-being practitioners. The service is anonymous at point of entry and free for users, and provides an extensive, person-centred approach which results in a wide range of user needs. Deriving outcome measures from the platform’s varied counselling sessions, aims to provide both insights into the want and needs of users and underpin improved mental health support. Objectives & Approach The objective of this research is to show how contemporary machine learning methods (Transformer Models and Contextualised Topic Modelling) may be combined with digital footprint data (in the form of seldom explored text data generated on DMHPs) to identify service user wants and needs. Specifically, with automated inference of patient outcomes currently scarce, we focus on the development of outcome measures in the context of ‘single sessions’, applying machine learning methods to extract topics related to the wants and needs of service users on DMHPs. Relevance to Digital Footprints The data used in this study consisted of transcripts between Qwell practitioners and service users (SU’s) (N=874) at conversation level (N=2323), a filter was applied to the dataset to ensure that focus the SUs elicitation of wants and needs fit into the criteria of a single session. Individuals in the final selected cohort (n=192) are not significantly different from the wider Qwell SU population in the study period in terms of age, gender or ethnicity; suggesting that the cohort is representative of the wider target population. This study shows the potential of mental health digital footprints data when providing insight into the wants and needs of DMHP SUs. Conclusions & Implications Via this analysis of mental health digital footprint data, this work establishes a process for creating a new outcome measure through the computational analysis of transcript data, incorporating insights from clinical experts and individuals with lived experience of engaging with DMHPs with textual data analysis. This methodological approach of Transformer Models and Contextualized Topic Modelling enables the analysis of a considerable volume of data faster than manually reviewing transcripts. We offer suggestions for the refinement of automated methods, in collaboration with direct support and feedback from both clinicians and individuals with lived experience of DMHPs to enable the understanding of wants and needs of service users within DMHPs.