A Novel BERT-based Classifier to Detect Political Leaning of YouTube
  Videos based on their Titles
Nouar AlDahoul Talal Rahwan Yasir Zaki
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.