The rise of AI technology in news broadcasting marks a significant change in information delivery, particularly for Gen Zers with unique media format preferences. While AI anchors aim to boost engagement, their effectiveness is often questioned due to inadequate implementations. This study posits that the success of AI anchors depends on the congruency between AI images and news content. Through a mixed-method investigation, we examine how different combinations of AI anchor images and news contents influence Gen Zers’ news-viewing experiences. The findings reveal that hyper-simulation anchors facilitate experiential engagement with soft news while hyper-realization anchors enable more comprehensive cognitive processing of hard news through perceived credibility. These effects are mediated by three fundamental mechanisms – processing alignment, emotional resonance, and perceived credibility. This research expands media congruency theory, offering strategic insights for optimizing AI anchor designs to better engage Gen Zers.

The scientific impact of academic papers is influenced by intricate factors such as dynamic popularity and inherent contribution. Existing models typically rely on static graphs for citation count estimation, failing to differentiate among its sources. In contrast, we propose distinguishing effects derived from various factors and predicting citation increments as estimated potential impacts within the dynamic context. In this research, we introduce a novel model, DPPDCC, which Disentangles the Potential impacts of Papers into Diffusion, Conformity, and Contribution values. It encodes temporal and structural features within dynamic heterogeneous graphs derived from the citation networks and applies various auxiliary tasks for disentanglement. By emphasizing comparative and co-cited/citing information and aggregating snapshots evolutionarily, DPPDCC captures knowledge flow within the citation network. Afterwards, popularity is outlined by contrasting augmented graphs to extract the essence of citation diffusion and predicting citation accumulation bins for quantitative conformity modeling. Orthogonal constraints ensure distinct modeling of each perspective, preserving the contribution value. To gauge generalization across publication times and replicate the realistic dynamic context, we partition data based on specific time points and retain all samples without strict filtering. Extensive experiments on three datasets validate DPPDCC's superiority over baselines for papers published previously, freshly, and immediately, with further analyses confirming its robustness. Our codes and supplementary materials can be found at GitHub (https://github.com/ECNU-Text-Computing/DPPDCC).