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http://dx.doi.org/10.6109/jkiice.2020.24.8.999

Analyzing Media Bias in News Articles Using RNN and CNN  

Oh, Seungbin (Incheon Academy of Science and Arts)
Kim, Hyunmin (Incheon Academy of Science and Arts)
Kim, Seungjae (Incheon Academy of Science and Arts)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.8, 2020 , pp. 999-1005 More about this Journal
Abstract
While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.
Keywords
National Assembly proceedings; News article; Deep learning; Keyword extraction; Bias;
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