Temporal and Content Coupling Analysis of Social Media User Behavior
For researchers studying user behavior in news recommendation systems, this work provides a detailed empirical analysis of temporal and content coupling, but it is incremental as it applies known methods to new data.
This study proposes a multi-scale temporal-content framework for analyzing news consumption behavior, validated on two real-world datasets (MIND and Adressa). Results reveal hierarchical temporal patterns (circadian rhythms, power-law session intervals, exponential within-session actions) and show that clicks are primarily driven by historical interests, with this dependence weakening as content diversity increases.
News consumption behavior is shaped by the coupling between temporal dynamics and content selection. This study proposes a multi-scale temporal-content framework and validates it on two large real-world news datasets, MIND and Adressa. Results reveal hierarchical temporal patterns. At the macroscale, Fourier modeling identifies clear circadian rhythms; at the mesoscale, session intervals follow a power-law distribution with $α\approx 1$; and at the microscale, within-session action counts and inter-action intervals follow exponential distributions with $λ\approx 0.3$ and $λ\approx 0.02$, respectively. Content analysis shows that clicks are mainly driven by historical interests, while this dependence weakens as content diversity increases. Temporal-content coupling further indicates that users' historical interests dominate active time periods in shaping behavior. Preference groups also differ: timeliness and entertainment-oriented users click more frequently and rely more on historical interests, whereas diversified users click less and are more sensitive to content diversity.