U-Belong Social Network Data: Egocentric Network Interviews and Survey Measures, 2023-2024

Byrom, Nicola and Slack, Hannah and Long, Emily (2026). U-Belong Social Network Data: Egocentric Network Interviews and Survey Measures, 2023-2024. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-858287

Loneliness is linked to poor mental health and reduced educational achievement and social mobility. It is often thought of as something experienced by the elderly. However, loneliness is a growing concern among university students. Recent studies have found that young people report high levels of loneliness. This seems puzzling. University students are surrounded by peers. They often live with friends and have many opportunities to socialise. Why would they feel lonely?

Addressing this question, we will develop the concept of loneliness. We will work with young people to represent the adolescent experience accurately and sensitively. We will work with students across the project, making co-creation a priority.

We will identify opportunities to reduce loneliness in university students. There are 1.7million adolescents in UK universities. As many in 2 in 5 students may meet criteria for mental illness. Increasingly, this is a cause for concern. Universities are looking for ways to support student mental health. Students are at a developmental transition and experience dramatic changes in social networks, creating risk for loneliness. However, if properly understood, loneliness may be reduced, providing a target to boost mental health and educational achievement. New interventions depend on a strong theoretical framework and researchers need suitable tools to measure loneliness.

We can all describe loneliness. The COVID-19 lockdowns gave many people new insights into the experience of loneliness. However, understanding of the concept, especially in young people, is limited. Historical analysis can help. We will explore when and how the idea of university as a social experience emerged. This will provide a broader social and cultural context to understand loneliness.

We will make it easier to measure loneliness sensitively. Loneliness is often assessed using a single question: "how often do you feel lonely?" This cannot identify differences in origin or experience. It does not capture how loneliness relates to social connection, sense of belonging or expectations. We will investigate these links and develop new tools to allow differences in loneliness to be understood.

We will look at how social contacts change as young people move to university and ask if these changes cause loneliness. To do this, we will make use of the rich, but under-used, Social Network Analysis. Because this approach is under-used, we will develop simplified resources to help the others capture key insights in surveys. We will develop a new measurement tool to assess expectations of social connection. We will use this to identify differences in student's expectations for social connection and ask how these expectations impact the experience of loneliness.

Students often describe belonging as the opposite of loneliness. Do students lack a of sense of belonging? Does this drive loneliness? We will test whether a sense of belonging helps us understand loneliness, over and above social networks and expectations for social connection. We will explore how the group dynamics that support a sense of belonging, especially for minority groups, may influence loneliness.

Social identity influences our sense of belonging. Therefore, in looking at belonging, as well as social connection and expectations, the diversity of the student population is key. Across our research we aim to understand the broad diversity of student experience and how this shapes differences in the experience of loneliness.

We will develop a rich and detailed theoretical framework for loneliness. We will test whether there are different types of loneliness and examine how diverse social identities shape the experience of loneliness. The project will develop new tools to facilitate future research into loneliness. Through prioritising co-creation, we will address barriers to engagement and create resources and guidance to accelerate student involvement in research.

Data description (abstract)

This deposit comprises two related egocentric social network datasets collected as part of the UK MRC–funded U-Belong project, which examines social belonging, social connection, and mental health among students in UK higher education. Together, these datasets capture both the structure and composition of students’ personal social networks and students’ perceptions and experiences of those networks, enabling detailed cross-sectional and longitudinal network analyses.

The first component (“From Familiar Faces”) consists of structured egocentric social network interview data collected from university students at up to two time points. Participants identified individuals (“alters”) in their personal social networks and provided information on relationship type using broad, pre-specified categories (e.g. friend, family member, romantic partner, classmate, flatmate, university staff), emotional closeness, frequency and mode of contact, and perceived support. Participants also reported whether alters knew one another, allowing the construction of alter–alter ties and the derivation of network structure measures such as network size, density, interconnectedness, and composition over time.

The second component comprises complementary survey-based social network measures. These data focus on aggregate and compositional characteristics of students’ social networks, including perceived network size, diversity, availability of support, and patterns of social connection across different relational contexts. These measures are designed to be analytically compatible with the interview-based network data, enabling comparison between detailed egocentric network structures and broader self-reported network characteristics.

Across both components, the datasets include participant-level variables (with pseudonymous identifiers enabling longitudinal linkage), alter-level variables (where applicable), tie-level indicators, and derived network measures. No names or free-text descriptions of network members are included. Relationship types are restricted to broad categorical descriptors, and temporal information has been minimised to reduce disclosure risk.

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