Summary of A Novel Bert-based Classifier to Detect Political Leaning Of Youtube Videos Based on Their Titles, by Nouar Aldahoul et al.
A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles
by Nouar AlDahoul, Talal Rahwan, Yasir Zaki
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel classifier uses BERT, a language model from Google, to identify the political leaning of YouTube videos based on their titles. The classifier is trained and validated using a public dataset of 10 million video titles, categorizing them into six categories: Far Left, Left, Center, Anti-Woke, Right, and Far Right. The results show that the proposed classifier achieves an accuracy of 75% and an F1 score of 77%, outperforming alternative classifiers. The classifier is further validated by applying it to video titles from prominent news agencies, with the predicted political leaning matching the agency’s known leanings for most cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to figure out if YouTube videos are conservative or liberal based on their titles. They used a special kind of artificial intelligence called BERT to look at 10 million video titles and decide which ones fit into different categories like far left, left, center, right, and far right. The AI was really good at getting it right – it was correct 75% of the time! To make sure it wasn’t just a one-time thing, they tested it on videos from big news sources like Fox News and New York Times. In most cases, the AI got it right too. |
Keywords
» Artificial intelligence » Bert » F1 score » Language model
