Papers › PoLYTC: a novel BERT-based classifier to detect political leaning of YouTube videos...
PoLYTC: a novel BERT-based classifier to detect political leaning of YouTube videos based on their titles
Nouar AlDahoul, Talal Rahwan, Yasir Zaki
Over two-thirds of the U.S. population uses YouTube, and a quarter of U.S. adults regularly receive their news from it. Despite the massive political content available on the platform, to date, no classifier has been proposed to classify the political leaning of YouTube videos. The only exception is a classifier that requires extensive information about each video (rather than just the title) and classifies the videos into just three classes (rather than the widely-used categorization into six classes). To fill this gap, “PoLYTC” (Political Leaning YouTube Classifier) is proposed to classify YouTube videos based on their titles into six political classes. PoLYTC utilizes a large language model, namely BERT, and is fine-tuned on a public dataset of 11.5 million YouTube videos. Experiments reveal that the proposed solution achieves high accuracy (75%) and high F1-score (77%), thereby outperforming the state of the art. To further validate the solution’s classification performance, several videos were collected from numerous prominent news agencies’ YouTube channels, such as Fox News and The New York Times, which have widely known political leanings. These videos were classified based on their titles, and the results have shown that, in the vast majority of cases, the predicted political leaning matches that of the news agency. PoLYTC can help YouTube users make informed decisions about which videos to watch and can help researchers analyze the political content on YouTube.
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