模拟用户观看时长以探究YouTube Shorts推荐中的偏差
Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations
AI总结：
本研究通过模拟用户在YouTube Shorts上的不同观看行为，结合超40万视频数据集和GPT-4o语义评估，揭示了推荐算法在观看时长影响下出现的放大、漂移与主题泛化现象，为平台内容多样性与问责制提供了多学科视角。
摘要：
YouTube Shorts等短视频平台日益塑造着信息消费的方式，然而由参与度驱动的算法对内容曝光的影响仍未被充分理解。本研究考察了不同观看行为（包括快速滑动或跳过）如何影响推荐视频的相关性与主题连续性。我们使用一个包含超过404,000个视频的数据集，在更广泛的地缘政治主题和更聚焦的冲突议题上模拟观众互动，涵盖与俄罗斯、中国、俄乌战争及南海争端相关的主题。我们评估了在不同观看时长条件下推荐链中相关性的变化，并使用GPT-4o评估视频之间的语义对齐程度。分析揭示了放大、漂移和主题泛化等模式，对内容多样性和平台问责制具有重要启示。通过桥接计算机科学、媒体研究和政治传播的视角，本研究为理解参与度信号如何在短视频内容生态系统中影响算法路径提供了多学科的认识。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
