A quarter of US adults regularly get their news from YouTube. Yet, despite
the massive political content available on the platform, to date no classifier
has been proposed to identify the political leaning of YouTube videos. To fill
this gap, we propose a novel classifier based on Bert -- a language model from
Google -- to classify YouTube videos merely based on their titles into six
categories, namely: Far Left, Left, Center, Anti-Woke, Right, and Far Right. We
used a public dataset of 10 million YouTube video titles (under various
categories) to train and validate the proposed classifier. We compare the
classifier against several alternatives that we trained on the same dataset,
revealing that our classifier achieves the highest accuracy (75%) and the
highest F1 score (77%). To further validate the classification performance, we
collect videos from YouTube channels of numerous prominent news agencies, such
as Fox News and New York Times, which have widely known political leanings, and
apply our classifier to their video titles. For the vast majority of cases, the
predicted political leaning matches that of the news agency.

美国约四分之一的成年人定期通过 YouTube 获取新闻内容。然而，尽管该平台上存在大量政治内容，但迄今为止还没有提出可以识别 YouTube 视频政治倾向的分类器。为了填补这一空白，我们提出了一种基于 Google 的语言模型 Bert 的新型分类器，仅通过视频标题将 YouTube 视频分为六类：极左、左派、中间、反唤醒、右派和极右。我们使用一个公共数据集中的 1000 万个 YouTube 视频标题（涵盖各种类别）来训练和验证所提出的分类器。我们将分类器与使用相同数据集训练的几种替代算法进行了比较，结果显示我们的分类器在准确性（75%）和 F1 分数（77%）方面都达到了最高水平。为了进一步验证分类性能，我们从包括 Fox News 和 New York Times 在内的多家知名新闻机构的 YouTube 频道收集视频，并将我们的分类器应用于它们的视频标题。在绝大多数情况下，预测的政治倾向与新闻机构相匹配。