论文标题
对Tiktok的个性化因素的实证研究
An Empirical Investigation of Personalization Factors on TikTok
论文作者
论文摘要
Tiktok目前是增长最快的社交媒体平台，拥有超过10亿个活跃的每月用户，大多数是Z世代。可以说，其最重要的成功驱动力是其推荐系统。尽管Tiktok的算法对平台的成功和内容分布非常重要，但对算法的经验分析几乎没有工作。我们的工作为填补这一研究差距奠定了基础。我们使用我们开发的自定义算法的袜子式审核方法，我们测试并分析了用于访问Tiktok，跟进和类似功能的语言和位置的效果，以及推荐内容在用户观看某些帖子时如何变化的时间比其他帖子更长。我们提供证据表明所有测试因素会影响tiktok用户建议的内容。此外，我们确定以下功能具有最大的影响力，其次是类似和视频视图率。我们还讨论了我们发现的含义，在过滤器气泡对Tiktok的形成和有问题的内容的增殖的背景下。
TikTok currently is the fastest growing social media platform with over 1 billion active monthly users of which the majority is from generation Z. Arguably, its most important success driver is its recommendation system. Despite the importance of TikTok's algorithm to the platform's success and content distribution, little work has been done on the empirical analysis of the algorithm. Our work lays the foundation to fill this research gap. Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok, follow- and like-feature, as well as how the recommended content changes as a user watches certain posts longer than others. We provide evidence that all the tested factors influence the content recommended to TikTok users. Further, we identified that the follow-feature has the strongest influence, followed by the like-feature and video view rate. We also discuss the implications of our findings in the context of the formation of filter bubbles on TikTok and the proliferation of problematic content.
