Embedding-Based Approaches to Hyperpartisan News Detection

TL;DR

This paper explores embedding-based methods for detecting hyperpartisan news, demonstrating that large language models significantly improve accuracy over previous approaches like ELMo with LSTM.

Contribution

It introduces the use of large language models for embedding generation in hyperpartisan news detection, achieving higher accuracy than traditional methods.

Findings

LLMs achieve around 92% accuracy in hyperpartisan news detection.

Previous ELMo-based systems achieved about 83% accuracy.

Embedding-based approaches outperform traditional n-grams and sentiment analysis.

Abstract

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.