# A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles

arXiv: https://arxiv.org/abs/2404.04261 · HTML: https://mlsanity.com/paper/2404.04261/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Multi-view Models for Political Ideology Detection of News Articles](https://mlsanity.com/paper/1809.03485/) (2018)
- [Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization](https://mlsanity.com/paper/1912.11211/) (2019)
- [Examining the consumption of radical content on YouTube](https://mlsanity.com/paper/2011.12843/) (2020)
- [Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks](https://mlsanity.com/paper/2207.08349/) (2022)
- [Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information](https://mlsanity.com/paper/1910.08948/) (2019)
- [Machine-Learning media bias](https://mlsanity.com/paper/2109.00024/) (2021)
- [Detecting Political Biases of Named Entities and Hashtags on Twitter](https://mlsanity.com/paper/2209.08110/) (2022)
- [Prediction of Political Leanings of Chinese Speaking Twitter Users](https://mlsanity.com/paper/2110.05723/) (2021)

## Built on by (most cited first, 2 of 2)

- [Benchmarking the Legal Reasoning of LLMs in Arabic Islamic Inheritance Cases](https://mlsanity.com/paper/2508.15796/) (2025)
- [Benchmarking the Medical Understanding and Reasoning of Large Language Models in Arabic Healthcare Tasks](https://mlsanity.com/paper/2508.15797/) (2025)

## Abstract (arXiv)

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.