Embedding-based Approaches to Hyperpartisan News Detection

• URL: http://arxiv.org/abs/2501.01370v1
• Date: Thu, 02 Jan 2025 17:29:53 GMT
• Title: Embedding-based Approaches to Hyperpartisan News Detection
• Authors: Karthik Mohan, Pengyu Chen,
• Abstract summary: Hyperpartisan news is news that takes an extremely polarized political standpoint with an intention of creating political divide among the public. Our best system using pre-trained ELMo with Bidirectional LSTM achieved an accuracy of 83% through 10-fold cross-validation.
• Score: 6.522338519818378
• License:
• 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 is news that 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.

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