Politicians-based Deep Learning Models for Detecting News, Authors and Media Political Ideology

Non-partisanship is one of the qualities that contribute to journalistic objectivity. Factual reporting alone cannot combat political polarization in the news media. News framing, agenda settings, and priming are influence mechanisms that lead to political polarization, but they are hard to identify. This paper attempts to automate the detection of two political science concepts in news coverage: politician personalization and political ideology. Politicians' news coverage personalization is a concept that encompasses one more of the influence mechanisms. Political ideologies are often associated with controversial topics such as abortion and health insurance. However, the paper prove that politicians' personalization is related to the political ideology of the news articles. Constructing deep neural network models based on politicians' personalization improved the performance of political ideology detection models. Also, deep networks models could predict news articles' politician personalization with a high F1 score. Despite being trained on less data, personalized-based deep networks proved to be more capable of capturing the ideology of news articles than other non-personalized models. The dataset consists of two politician personalization labels, namely Obama and Trump, and two political ideology labels, Democrat and Republican. The results showed that politicians' personalization and political polarization exist in news articles, authors, and media sources.