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Embedding-based Approaches to Hyperpartisan News Detection

Machine Learning
NLP

Karthik Mohan, Pengyu Chen, “Embedding-based Approaches to Hyperpartisan News Detection”, doi: 10.48550/arXiv.2501.01370

Authors
Affiliation

Karthik Mohan

University of Toronto

Pengyu Chen

University of Toronto

Published

October 2024

Doi

10.48550/arXiv.2501.01370

Abstract

In this paper, we describe our 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. We attempted several approaches, including n-grams, sentiment analysis, as well as sentence and document representation using pre-tained ELMo. Our best system using pre-trained ELMo with Bidirectional LSTM achieved an accuracy of 83% through 10-fold cross-validation without much hyperparameter tuning.

@misc{mohan2025embeddingbasedapproacheshyperpartisannews,
      title={Embedding-based Approaches to Hyperpartisan News Detection}, 
      author={Karthik Mohan and Pengyu Chen},
      year={2025},
      eprint={2501.01370},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2501.01370}, 
}