Embedding-Based Approaches to Hyperpartisan News Detection
This work addresses the problem of identifying hyperpartisan news articles to combat political polarization, presenting an incremental improvement over existing methods.
The paper tackles hyperpartisan news detection by comparing various embedding approaches, with the best system using LLMs for embedding generation achieving 92% accuracy, outperforming the previous best system using pre-trained ELMo with Bidirectional LSTM at 83% accuracy.
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.