Hyperpartisan News and Articles Detection Using BERT and ELMo

Huang, Gerald Ki Wei and Lee, Jun Choi (2020) Hyperpartisan News and Articles Detection Using BERT and ELMo. 2019 International Conference on Computer and Drone Applications (IConDA). pp. 29-32. ISSN 978-1-7281-6593-6

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Abstract

Fake news and articles are misleading the readers. This leads to the increasing studies of fake news article detection over the decades. Hyperpartisan news is news riddled with twisted and untruth and extremely one-sided. This news can spread more successfully than others. Besides that, hyperpartisan news can mimic the form of regular news articles. This study aims to identify and classify the hyperpartisan news with BERT and ELMo. Two distinct models, BERT and ELMo, were created to classify hyperpartisan news from two datasets, namely by-article and by-publisher. Few other models with different settings and training designed to test and optimise the performance of both models. The results of the optimised BERT and ELMo models can achieve 68.4% and 60.8%, respectively.

Item Type: Article
Uncontrolled Keywords: Natural Language Processing, Classification, Hyperpartisan, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Mr. Jun Choi Lee
Date Deposited: 30 Nov 2020 05:27
Last Modified: 30 Nov 2020 05:28
URI: http://ir.unimas.my/id/eprint/33126

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