Analysis of The Irish Times Newspaper Articles

ABSTRACT

The number of fake news created and shared has rapidly increased during the COVID-19 [9]. This paper analyzes the articles from the Irish Times using IBM Cognos Analytics. Its main goal is to find the trends of headline news through the years by different topics and relevant keywords. Almost 1.5 million headlines of the Irish Times from January 1st, 1996 to December 31st, 2019 were collected and analyzed. The contents of each headline were cleaned and matched with three different topics (War, Natural Disasters, and Irish Politics) based on keywords representative from each of these topics. We identified trends for each of the topics analyzed for the number of articles published throughout 1996 to 2019, and correlations with particular historical events. The results showed that the news section has been the most abundant in the Irish Times. In addition, results also have revealed the frequency of Politics keywords increase whenever election seasons approach, and the frequency of natural disasters keywords increase when natural disasters occur. This research can be implemented to see war, natural disasters, and the political side of Ireland and infer from its frequencies. There has been much research on the headlines of newspapers in general by country, and by specific topics like traffic accidents [7], or areas of sentiment analysis. This research is a part of sentiment analysis, more focused on The Irish Times' news headline opinion mining. © 2022 ACM.