# Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

arXiv: https://arxiv.org/abs/1910.08948 · HTML: https://mlsanity.com/paper/1910.08948/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2109.05184/) (2021)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence](https://mlsanity.com/paper/2109.05325/) (2021)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data](https://mlsanity.com/paper/2306.05535/) (2023)
- [Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels](https://mlsanity.com/paper/2108.12519/) (2021)
- [A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles](https://mlsanity.com/paper/2404.04261/) (2024)
- [Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection](https://mlsanity.com/paper/2405.18974/) (2024)
- [Social and Political Framing in Search Engine Results](https://mlsanity.com/paper/2507.13325/) (2025)

## Abstract (arXiv)

We address the problem of predicting the leading political ideology, i.e., left-center-right bias, for YouTube channels of news media. Previous work on the problem has focused exclusively on text and on analysis of the language used, topics discussed, sentiment, and the like. In contrast, here we study videos, which yields an interesting multimodal setup. Starting with gold annotations about the leading political ideology of major world news media from Media Bias/Fact Check, we searched on YouTube to find their corresponding channels, and we downloaded a recent sample of videos from each channel. We crawled more than 1,000 YouTube hours along with the corresponding subtitles and metadata, thus producing a new multimodal dataset. We further developed a multimodal deep-learning architecture for the task. Our analysis shows that the use of acoustic signal helped to improve bias detection by more than 6% absolute over using text and metadata only. We release the dataset to the research community, hoping to help advance the field of multi-modal political bias detection.