Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination
Abdul Sittar, Daniela Major, Caio Mello, Dunja Mladenic, and Marko, Grobelnik

TL;DR
This study analyzes global COVID-19 news coverage to reveal how reporting varies across countries with different political and economic contexts, using advanced topic modeling and sentiment analysis techniques.
Contribution
It introduces a novel approach combining TF-IDF pooling and LDA with ngrams to analyze COVID-19 news across diverse geopolitical landscapes.
Findings
News reporting aligns with political orientations.
Economic issues reported depend on local economic conditions.
Sentiment analysis reveals differences across regions.
Abstract
The purpose of this study is to analyse COVID-19 related news published across different geographical places, in order to gain insights in reporting differences. The COVID-19 pandemic had a major outbreak in January 2020 and was followed by different preventive measures, lockdown, and finally by the process of vaccination. To date, more comprehensive analysis of news related to COVID-19 pandemic are missing, especially those which explain what aspects of this pandemic are being reported by newspapers inserted in different economies and belonging to different political alignments. Since LDA is often less coherent when there are news articles published across the world about an event and you look answers for specific queries. It is because of having semantically different content. To address this challenge, we performed pooling of news articles based on information retrieval using TF-IDF…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Computational and Text Analysis Methods
MethodsLinear Discriminant Analysis
