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.
@article{arxiv.2501.01370,
title = {Embedding-Based Approaches to Hyperpartisan News Detection},
author = {Karthik Mohan},
journal= {arXiv preprint arXiv:2501.01370},
year = {2025}
}
Comments
Updated version reflecting sole authorship. All coauthor contributions have been removed. Experimental corrections and analysis updates were introduced in the original version and are retained here as part of the submitter's independent work, along with expanded experiments by the submitter