Abstract
Non-partisanship is one of the qualities that con-tribute 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.
Highlights
This paper examines the relationship between two political science concepts that usually addressed separately, news articles’ political ideology and personalization
Personalization attributes such as politician visibility, personal traits, and reoccurring topics form a pattern that statistical models learn to distinguish personalized articles. articles’ tags are used in building the dataset, which indicate that the politician is directly related to the topic covered in the article
The deep neural networks trained on articles tagged with politicians, namely Trump and Obama
Summary
This paper examines the relationship between two political science concepts that usually addressed separately, news articles’ political ideology and personalization. Our main hypothesis is that by constructing intelligent models trained on politician centered articles will improve their performance in detecting articles’ political ideologies. Personalization attributes such as politician visibility, personal traits, and reoccurring topics form a pattern that statistical models learn to distinguish personalized articles. Introducing intelligent models to detect articles’ ideology based on their personalization proved to advance their performance. Articles’ personalization and ideology detection models can help the news recommendation systems mitigate proattitudinal selective exposure. A new approach is proposed to improve the performance of news political ideology detection models by building models with feature space extracted from politicians’ personalized articles. The detection models were evaluated against news articles, authors, and media sources to examine the relations between the two political concepts.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
