通过基于订阅的渠道嵌入了解YouTube社区
Understanding YouTube Communities via Subscription-based Channel Embeddings
摘要：
YouTube是全球新闻和娱乐的重要来源，但其规模使得研究该平台上讨论的想法和主题具有挑战性。本文提出了新的方法来发现和分类YouTube频道，使社区和类别的分析平台上使用的数量级更多的渠道比以前的研究中使用的。与其他研究人员使用频道和视频数据作为分类特征不同，这些方法使用了一种自监督学习方法，该方法利用了评论者的公共订阅页面。我们在预测YouTube新闻频道的政治倾向的任务上测试了分类方法，发现它在该任务上的表现优于以前的最佳模型。进一步的实验还表明，使用评论者订阅来发现频道有重要的优势。订阅数据，以及迭代的方法，应用于发现，我们目前的理解，最全面的一套英语社会政治YouTube频道尚未分析。我们尝试使用先前注释的数据集来预测通道的更细粒度的政治标签，并发现我们的模型在大多数顶级标签上的表现优于平均个人评论者。然后将这种细粒度的政治标签模型应用于新发现的英语社会政治频道，以创建一个新的数据集来分析不同政治内容的流量。数据显示，一些标签，如“党派权利”和“阴谋”，在只看最受欢迎的社会政治渠道时，代表性明显不足。通过使用我们的方法，我们能够更准确地了解YouTube上这些社区的规模。
英文摘要：
YouTube is an important source of news and entertainment worldwide, but the scale makes it challenging to study the ideas and topics being discussed on the platform. This paper presents new methods to discover and classify YouTube channels which enable the analysis of communities and categories on the platform using orders of magnitude more channels than have been used in previous studies. Instead of using channel and video data as features for classification as other researchers have, these methods use a self-supervised learning approach that leverages the public subscription pages of commenters. We test the classification method on the task of predicting the political lean of YouTube news channels and find that it outperforms the previous best model on the task. Further experiments also show that there are important advantages to using commenter subscriptions to discover channels. The subscription data, along with an iterative approach, is applied to discover, to our current understanding, the most comprehensive set of English language socio-political YouTube channels yet to be analyzed. We experiment with predicting more fine grained political tags for channels using a previously annotated dataset and find that our model performs better than the average individual human reviewer for most of the top tags. This fine grained political tag model is then applied to the newly discovered English language socio-political channels to create a new dataset to analyze the amount of traffic going to different political content. The data shows that some tags, such as "Partisan Right" and "Conspiracy", are significantly under represented when looking only at the most popular socio-political channels. Through the use of our methods, we are able to get a much more accurate picture of the size of these communities on YouTube.
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。
