Temporal and Content Coupling Analysis of Social Media User Behavior

TL;DR

This paper introduces a multi-scale framework to analyze how temporal patterns and content preferences influence social media news consumption, revealing hierarchical behaviors and user differences.

Contribution

It proposes a novel multi-scale temporal-content analysis framework validated on large datasets, uncovering hierarchical temporal patterns and user behavior distinctions.

Findings

Hierarchical temporal patterns identified at macro, meso, and micro scales.

Circadian rhythms and session interval distributions follow specific statistical models.

User interests and content diversity influence clicking behavior and temporal dependence.

Abstract

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 $\alpha \approx 1$; and at the microscale, within-session action counts and inter-action intervals follow exponential distributions with $\lambda \approx 0.3$ and $\lambda \approx 0.02$, 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.