Lonely Individuals Show Distinct Patterns of Social Media Engagement

TL;DR

This study investigates how objective, cross-device social-media use relates to loneliness over six months in a German panel. By merging web-trace data from mobile and desktop with monthly UCLA-3 loneliness measures and analyzing via linear mixed-effects models, the authors show that greater social-media time is associated with higher loneliness, independent of total online time. Temporal engagement patterns differ by device, with lonely individuals on desktop displaying more frequent but shorter sessions, and platform-type use on mobile showing greater engagement with networking, visual-sharing, messaging, and relationship-oriented apps. The findings highlight the value of digital traces for digital-well-being research and have implications for interventions and platform design that account for device and platform context in addressing loneliness.

Abstract

Loneliness has reached epidemic proportions globally, posing serious risks to mental and physical health. As social media platforms increasingly mediate social interaction, understanding their relationship with loneliness has become urgent. While survey-based research has examined social media use and loneliness, findings remain mixed, and little is known about when and how often people engage with social media, or about whether different types of platforms are differently associated with loneliness. Web trace data now enable objective examination of these behavioral dimensions. We asked whether objectively measured patterns of social media engagement differ between lonely and non-lonely individuals across devices and platform types. Analyzing six months of web trace data combined with repeated surveys ($N=589$ mobile users; $N=851$ desktop users), we found that greater social media use was associated with higher loneliness across both devices, with this relationship specific to social media rather than other online activities. On desktop, lonely individuals exhibited shorter sessions but more frequent daily engagement. Lonely individuals spent more time on visual-sharing ($g = -0.47$), messaging ($g = -0.36$), and networking-oriented platforms on mobile. These findings demonstrate how longitudinal web trace data can reveal behavioral patterns associated with loneliness, and more broadly illustrate the potential of digital traces for studying other psychological states. Beyond research, the results inform the responsible design of digital interventions and platform features that better support psychological well-being across different technological contexts.

Figures (5)

• Figure 1: Effect sizes for desktop and mobile temporal patterns comparing lonely and non-lonely individuals. Effect sizes are presented as Hedges' g with 90% confidence intervals (error bars). Negative values indicate higher values in the lonely group; positive values indicate higher values in the non-lonely group. Red points indicate effects exceeding the SESOI ($\mid g\mid\geq0.4$). a, Desktop use showed meaningful differences in daily sessions and session duration. b, Mobile use showed no meaningful differences across all metrics.
• Figure 2: Effect sizes for types of social media use comparing lonely and non-lonely individuals. Effect sizes are presented as Hedges' g with 90% confidence intervals (error bars). Negative values indicate higher use in the lonely group; positive values indicate higher use in the non-lonely group. Red points indicate effects exceeding the SESOI ($\mid g\mid\geq0.4$). a, Desktop use showed a meaningful difference in networking-oriented platforms. b, Mobile use showed meaningful differences across networking-oriented, visual-sharing, relationship-oriented, and messaging platforms.
• Figure 3: Participant recruitment and exclusion process. Participants could contribute data to both analyses if they met the quality criteria for both devices.
• Figure 4: Mixed-method longitudinal study design for investigating digital behavior and loneliness. The study employed a 6-month longitudinal panel design combining continuous objective data collection with loneliness assessment. Upper panel: objective web trace data were continuously collected via desktop web browsing logs and mobile application usage logs, and participants completed monthly online surveys across six waves (Wave 1: UCLA-3 + demographics + MEQ; Waves 2--6: UCLA-3 only). Lower panel: extracted features for each hypothesis. $N = 1,490$, Germany, 2023--2024.
• Figure 5: Heat map visualization of preprocessing method stability across datasets and overall method ranking. Method performance is shown by the dataset, with green indicating more stable (lower deviation) methods and red indicating less stable methods. Results support the selection of z-score outlier removal as the primary preprocessing approach for group comparisons.