对比YouTube长短视频推荐算法，发现短内容更易导致信息窄化。
Efficient Data Retrieval and Comparative Bias Analysis of Recommendation Algorithms for YouTube Shorts and Long-Form Videos
- 用并行计算和爬虫技术高效获取数据，突破API限制。
- 短视频推荐更快速转向高互动内容，多样性显著降低。
- 在敏感议题上算法会放大特定观点，影响舆论生态。
短视频内容（如YouTube Shorts）的兴起重塑了用户在线参与方式，引发对推荐算法如何塑造用户体验的深刻关注。这些算法虽显著影响内容消费，但偏见、回音室效应与内容多样性不足等问题依然存在。本研究构建了高效的データ收集框架，通过并行计算与先进爬虫技术克服YouTube API的局限性，分析了长短视频推荐算法的差异。结果表明，短视频推荐表现出更快的内容转向，倾向于高互动但多样性较低的视频；而在涉及政治敏感话题（如南海争端）时，算法显示出明显的偏向性，可能加剧特定叙事的传播。研究为设计更公平透明的推荐系统提供了可操作建议，强调在数字媒体演进中践行负责任的AI至关重要。
原文摘要 · Abstract (English)
The growing popularity of short-form video content, such as YouTube Shorts, has transformed user engagement on digital platforms, raising critical questions about the role of recommendation algorithms in shaping user experiences. These algorithms significantly influence content consumption, yet concerns about biases, echo chambers, and content diversity persist. This study develops an efficient data collection framework to analyze YouTube's recommendation algorithms for both short-form and long-form videos, employing parallel computing and advanced scraping techniques to overcome limitations of YouTube's API. The analysis uncovers distinct behavioral patterns in recommendation algorithms across the two formats, with short-form videos showing a more immediate shift toward engaging yet less diverse content compared to long-form videos. Furthermore, a novel investigation into biases in politically sensitive topics, such as the South China Sea dispute, highlights the role of these algorithms in shaping narratives and amplifying specific viewpoints. By providing actionable insights for designing equitable and transparent recommendation systems, this research underscores the importance of responsible AI practices in the evolving digital media landscape.
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