Authors:
Dexter Dave R. Valdeavilla
and
Maria Teresa R. Pulido
Affiliation:
Department of Physics, Mapúa University, Intramuros, Manila City and 1002 Philippines
Keyword(s):
Media, Large-scale Information Systems and Applications, Data Processing, Artificial Intelligence, Big Data Algorithm, Social Science and Implications for Big Data, Data Analytics, Opinion Mining, Analytics as a Service (AaaS).
Abstract:
Newspapers provide factual reports on current events. However, news media has been shown to be ideologically biased, often negatively shaping the readers' point of view. News on controversial issues makes the bias of the newspaper or its writers more visible. This study aims to measure the objectivity of newspapers by classifying news articles from three newspaper agencies covering the 2017 Battle of Marawi in Southern Philippines. We used Aylien Sentiment Analysis Tool to detect the bias or polarity in each news article (whether positive, negative or neutral). Negative articles on Marawi dominated the three broadsheets (45.1% to 59.9%) while the neutral articles were the least frequent (16.1% to 21.2%). These results indicate that newspapers apply unequal space on the different sides of an issue, which may lead to unbalanced reporting. We also note that despite the varying number of total articles, the three papers applied the same proportion of positive, negative and neutral articl
es, which may imply collusion. The emergence of Big Data greatly increases the speed of gathering news articles on any given issue, while the Internet of Things enables readers and journalists to measure the objectivity of the news.
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