# Embedding-Based Approaches to Hyperpartisan News Detection

arXiv: https://arxiv.org/abs/2501.01370 · HTML: https://mlsanity.com/paper/2501.01370/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)

## Built on by (most cited first, 1 of 1)

- [Bangla BERT for Hyperpartisan News Detection: A Semi-Supervised and Explainable AI Approach](https://mlsanity.com/paper/2507.21242/) (2025)

## Abstract (arXiv)

In this report, I describe the systems in which the objective is to determine whether a given news article could be considered as hyperpartisan. Hyperpartisan news takes an extremely polarized political standpoint with an intention of creating political divide among the public. Several approaches, including n-grams, sentiment analysis, as well as sentence and document representations using pre-tained ELMo models were used. The best system is using LLMs for embedding generation achieving an accuracy of around 92% over the previously best system using pre-trained ELMo with Bidirectional LSTM which achieved an accuracy of around 83% through 10-fold cross-validation.