Stop Fiddling With Your Phone and Go Offline: People Experiencing High Information Overload Have Sparse Online Sessions

Paper Title

"Stop Fiddling With Your Phone and Go Offline: People Experiencing High Information Overload Have Sparse Online Sessions"

Publication Info

• Topic area: Interaction between web behavior and information overload in everyday contexts.
• Keywords: Information overload, web behavior, session sparseness, digital well-being, mobile usage, multitasking, longitudinal study, human-computer interaction, online habits, behavioral patterns.

Background and Problem

• Problem / challenge: Few quantitative studies have explored how information overload relates to everyday online behaviors, particularly in longitudinal contexts. Existing research often focuses on qualitative or cross-sectional surveys, leaving gaps in understanding the temporal and behavioral patterns associated with overload.
• Significance: Understanding these patterns can inform the design of digital well-being tools and interventions to mitigate the negative effects of information overload, which is linked to decreased well-being and performance.
• Motivation and related work: Prior studies have highlighted the prevalence of information overload in various contexts (e.g., email, social media, web browsing) and its association with stress and cognitive burden. However, the relationship between subjective overload experiences and objective web behaviors remains underexplored, particularly in multi-device, everyday contexts.

Solution

• Proposed approach: A longitudinal observational study combining web activity tracking and self-reported surveys to investigate the relationship between information overload and web behaviors.
• Novelty:
  1. Identification of session sparseness (repetitive, short-duration usage) as a key behavioral signal differentiating highly overloaded individuals.
  2. Exploration of how mobile and desktop usage durations relate to overload, revealing that mobile usage predicts overload while desktop usage does not.
  3. Analysis of how overload impacts subsequent web behaviors, showing limited behavioral adjustments despite high overload.
  4. Cluster analysis to identify user profiles based on overload levels and web behaviors.
• Procedure and key techniques:
  1. Tracked web activity (13.8M events) from 277 participants over seven months using desktop and mobile devices.
  2. Conducted four waves of surveys measuring information overload and related factors.
  3. Computed metrics such as session sparseness, event entropy, and usage durations across different time windows.
  4. Applied linear mixed-effects modeling and cluster analysis to identify patterns and user profiles.

Results

• Concrete findings:
  • High session sparseness strongly predicts information overload, with highly overloaded individuals exhibiting repetitive, short-duration usage.
  • Mobile usage, particularly in the morning, predicts overload, while desktop usage does not.
  • Specific content categories (e.g., social media, news) do not consistently predict overload.
  • Overload levels are stable over time, with large individual differences.
• Advantage over baselines:
  • Session sparseness provides a more nuanced and actionable behavioral signal for detecting overload compared to total usage duration.
  • Temporal patterns of usage (e.g., morning mobile use) are more predictive of overload than content-specific durations.
• Experiments / evaluation:
  • Data collected from 277 participants (55.6% female, median age group 55–64) in Germany.
  • Linear mixed-effects models evaluated the relationship between overload and web behaviors across different time windows (1–31 days).
  • Cluster analysis identified four user profiles, including a "highly overloaded" group characterized by high session sparseness.
• Limitations and future work:
  • Observational design limits causal inferences; future studies could incorporate experimental manipulations.
  • Exclusion of participants using untracked devices may underestimate total online activity.
  • Future research could explore additional factors like personality traits, cognitive abilities, and contextual influences on overload.

Summary

This study investigates the relationship between web behaviors and information overload through a longitudinal analysis of 277 participants' device usage and survey responses. Key findings highlight session sparseness (repetitive, short-duration usage) as a strong predictor of overload, with mobile usage (especially in the morning) playing a significant role. Despite experiencing overload, individuals show limited behavioral adjustments, suggesting that overload is embedded in habitual patterns. These insights inform both theoretical models of information overload and practical interventions, such as digital well-being tools that monitor and mitigate session sparseness to promote healthier online habits.