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.

翻译：新闻消费行为受到时间动态与内容选择之间耦合关系的影响。本研究提出了一种多尺度时序-内容分析框架，并在两个大规模真实新闻数据集MIND和Adressa上进行了验证。结果揭示了层次化的时序模式：在宏观尺度上，傅里叶建模识别出明显的昼夜节律；在中观尺度上，会话间隔遵循指数约为$\alpha\approx 1$的幂律分布；在微观尺度上，会话内的操作次数与操作间隔分别遵循指数约为$\lambda\approx 0.3$和$\lambda\approx 0.02$的指数分布。内容分析表明，点击行为主要受历史兴趣驱动，且这种依赖性随着内容多样性的增加而减弱。时序-内容耦合分析进一步揭示，用户的历史兴趣在活跃时段主导行为形成。偏好群体之间也存在差异：时效性与娱乐导向型用户点击频率更高，且更依赖历史兴趣，而多样化导向型用户点击较少，对内容多样性更为敏感。