Exploring the Role of AI Anchor Image-News Content Congruency in News-Viewing Experience Among Gen Zers: A Mixed-Method Study in the Chinese Context
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".