标题： 模拟用户观看时间以研究YouTube短视频推荐中的偏差 显示英文标题

标题： Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations

摘要： 短视频平台如YouTube Shorts日益影响信息的消费方式，但以参与度驱动的算法对内容曝光的影响仍缺乏深入理解。 本研究探讨了不同的观看行为，包括快速滚动或跳过，如何影响推荐视频的相关性和主题连续性。 使用包含404,000多条视频的数据集，我们模拟了观众在更广泛的地缘政治主题和更具体冲突中的交互，包括与俄罗斯、中国、俄乌战争以及南海争端相关的主题。 我们通过GPT-4o评估视频之间的语义一致性，分析在不同观看时间条件下相关性如何在推荐链中变化。 我们的分析揭示了放大、偏离和主题泛化的模式，这对内容多样性和平台责任具有重要意义。 通过结合计算机科学、媒体研究和政治传播的视角，这项工作为参与度提示如何影响短视频内容生态系统中的算法路径提供了跨学科的理解。 显示英文摘要

摘要： Short-form video platforms such as YouTube Shorts increasingly shape how information is consumed, yet the effects of engagement-driven algorithms on content exposure remain poorly understood. This study investigates how different viewing behaviors, including fast scrolling or skipping, influence the relevance and topical continuity of recommended videos. Using a dataset of over 404,000 videos, we simulate viewer interactions across both broader geopolitical themes and more narrowly focused conflicts, including topics related to Russia, China, the Russia-Ukraine War, and the South China Sea dispute. We assess how relevance shifts across recommendation chains under varying watch-time conditions, using GPT-4o to evaluate semantic alignment between videos. Our analysis reveals patterns of amplification, drift, and topic generalization, with significant implications for content diversity and platform accountability. By bridging perspectives from computer science, media studies, and political communication, this work contributes a multidisciplinary understanding of how engagement cues influence algorithmic pathways in short-form content ecosystems.