# Multi-view Models for Political Ideology Detection of News Articles

arXiv: https://arxiv.org/abs/1809.03485 · HTML: https://mlsanity.com/paper/1809.03485/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 27 of 27)

- [Analyzing Online Political Advertisements](https://mlsanity.com/paper/2105.04047/) (2021)
- [Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting](https://mlsanity.com/paper/2310.13297/) (2023)
- [Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity](https://mlsanity.com/paper/2104.08829/) (2021)
- [Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper](https://mlsanity.com/paper/2310.16269/) (2023)
- [Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles](https://mlsanity.com/paper/2406.10773/) (2024)
- [Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy](https://mlsanity.com/paper/2304.02247/) (2023)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [Modeling Multi-level Context for Informational Bias Detection by Contrastive Learning and Sentential Graph Network](https://mlsanity.com/paper/2201.10376/) (2022)
- [PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder](https://mlsanity.com/paper/2505.24646/) (2025)
- [Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts](https://mlsanity.com/paper/2506.12552/) (2025)
- [All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison](https://mlsanity.com/paper/2310.18827/) (2023)
- [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)
- [When Bigger Isn't Better: A Comprehensive Fairness Evaluation of Political Bias in Multi-News Summarisation](https://mlsanity.com/paper/2604.21309/) (2026)
- [A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches](https://mlsanity.com/paper/2109.09014/) (2021)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start](https://mlsanity.com/paper/2304.03414/) (2023)
- [Learning Unbiased News Article Representations: A Knowledge-Infused Approach](https://mlsanity.com/paper/2309.05981/) (2023)
- [Target-Aware Contextual Political Bias Detection in News](https://mlsanity.com/paper/2310.01138/) (2023)
- [A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers](https://mlsanity.com/paper/2311.01712/) (2023)
- [Left, Right, or Center? Evaluating LLM Framing in News Classification and Generation](https://mlsanity.com/paper/2601.05835/) (2026)
- [Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach](https://mlsanity.com/paper/2106.14387/) (2021)
- [Text and author-level political inference using heterogeneous knowledge representations](https://mlsanity.com/paper/2206.12293/) (2022)
- [Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data](https://mlsanity.com/paper/2207.00489/) (2022)
- [Top Gear or Black Mirror: Inferring Political Leaning From Non-Political Content](https://mlsanity.com/paper/2208.05662/) (2022)
- [Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis](https://mlsanity.com/paper/2211.02269/) (2022)
- [Modeling Political Orientation of Social Media Posts: An Extended Analysis](https://mlsanity.com/paper/2311.12323/) (2023)

## Abstract (arXiv)

A news article's title, content and link structure often reveal its political ideology. However, most existing works on automatic political ideology detection only leverage textual cues. Drawing inspiration from recent advances in neural inference, we propose a novel attention based multi-view model to leverage cues from all of the above views to identify the ideology evinced by a news article. Our model draws on advances in representation learning in natural language processing and network science to capture cues from both textual content and the network structure of news articles. We empirically evaluate our model against a battery of baselines and show that our model outperforms state of the art by 10 percentage points F1 score.