Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination (original) (raw)
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Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach
IEEE Access
Newspapers are very important for a society as they inform citizens about the events around them and how they can impact their life. Their importance becomes more crucial and indispensable in the times of health crisis such as the current COVID-19 pandemic. Since the starting of this pandemic newspapers are providing rich information to the public about various issues such as the discovery of a new strain of coronavirus, lockdown and other restrictions, government policies, and information related to the vaccine development for the same. In this scenario, analysis of emergent and widely reported topics/themes/issues and associated sentiments from various countries can help us better understand the COVID-19 pandemic. In our research, the database of more than 100,000 COVID-19 news headlines and articles were analyzed using top2vec (for topic modeling) and RoBERTa (for sentiment classification and analysis). Our topic modeling results highlighted that education, economy, US, and sports are some of the most common and widely reported themes across UK, India, Japan, South Korea. Further, our sentiment classification model achieved 90% validation accuracy and the analysis showed that the worst affected country, i.e. the UK (in our dataset) also has the highest percentage of negative sentiment.
How Did Europe's Press Cover Covid-19 Vaccination News? A Five-Country Analysis
Proceedings of the 1st International Workshop on Multimedia AI against Disinformation
Understanding how high-quality newspapers present and discuss major news plays a role towards tackling disinformation, as it contributes to the characterization of the full ecosystem in which information circulates. In this paper, we present an analysis of how the European press treated the Covid-19 vaccination issue in 2020-2021. We first collected a dataset of over 50,000 online articles published by 19 newspapers from five European countries over 22 months. Then, we performed analyses on headlines and full articles with natural language processing tools, including named entity recognition, topic modeling, and sentiment analysis, to identify main actors, subtopics, and tone, and to compare trends across countries. The results show several consistencies across countries and subtopics (e.g. a prevalence of neutral tone and relatively more negative sentiment for non-neutral articles, with few exceptions like the case of vaccine brands), but also differences (e.g., distinctly high negative-to-positive ratios for the no-vax subtopic.) Overall, our work provides a point of comparison to other news sources on a topic where disinformation and misinformation have resulted in increased risks and negative outcomes for people's health. CCS CONCEPTS • Computing methodologies → Natural language processing; • Information systems → Data mining.
Evaluation of the Content of National News Reflected on the Internet about Covid-19 Pandemic
Aurum Journal of Health Sciences, 2020
The coronavirus, which started in China and spreads almost all over the world, and whose source is not known exactly, but is regarded as illegally sold, can have a fatal effect on humans. Today, the number of people caught with coronavirus (Covid-19) is rapidly increasing. In order to eliminate this disease caused by the coronavirus, quarantine applications are carried out in many countries, curfews, curfews out of the country or out of the city, wear a mask, etc. Health professionals work day and night. However, the vaccine of the coronavirus has still not been found. In response to this situation, the World Health Organization has declared the Covid-19 epidemic as a pandemic. This study was carried out with the aim of evaluating the national news published in digital media regarding the coronavirus, which is declared as a pandemic worldwide. For this purpose, the keywords "Coronavirus, Koronavirüs, Covid-19, Pandemi, Covid 19, Kovid-19" were entered into the Google search engine. January 1, 2020-May 1, 2020 during the ten newspaper archives can be accessed on the number of the highest circulation national news website in Turkey, 150 news content was evaluated. In the study, content analysis, one of the qualitative research techniques, was used to analyze the data. As a result of the research, it has been determined that the most handled issues regarding Covid-19 in the news sites of the related newspapers are general information about Covid-19, about the current situation in Turkey coronavirus, the effects of coronavirus and that the coronavirus in the world, respectively. It has been determined that the least discussed issues in the news sites of the related newspapers are: the statements of the World Health Organization (WHO) regarding the coronavirus, the criticisms about the corona virus, post-pandemic life and coronavirus data presented in famous individuals.
Studia i Analizy Nauk o Polityce, 2021
Our study aims to understand the mutability of virus-related discourses by tracing common points of reference. To do so, we chose three newspapers from as many European states and monitored each mid-month Wednesday during the first wave of Covid-19: January to October 2020. The newspapers investigated were those with the largest audience: Corriere della Sera (Italy), Das Bild (Germany), and The Sun (United Kingdom). To do so, we used categories such as context, frame, and theme. We sifted the corpus, comprising 1175 articles, with Atlas.ti. Based on the categories used and their frequency, we reconstructed contextualization, framing, and thematization – all at a more abstract level. On content revolving around the keywords Covid-19 and Coronavirus, the only differences that emerged were a greater interest in sports for The Sun and vacations for Das Bild. All the newspapers considered granting little space to the weakest areas of the population: disabled or young people, women, immig...
Analysis of COVID-19 Coverage in Bangladesh News Media Using Topic Modelling
2020
Coronavirus Disease-2019, commonly known as COVID-19, originated from China has spread all over the globe including Bangladesh. News media of this country have been reporting both local and global news about the spread of COVID-19 since January 2020. Among news media, newspaper plays an important role to establish communication among health authorities, local government and people in risk issues like COVID-19. In this study, we have collected Bangladesh newspaper articles dated from 01 January 2020 to 30 April 2020 and have analyzed their coverage of COVID-19 applying topicmodelling technique. The median value of presence rate of daily COVID-19 related articles is increased from 4.2% to 67.5% after confirmed cases in Bangladesh. Newspaper has frequently reported about the spread of COVID-19 (number of tests, cases, deaths and recoveries) and healthcare system of different countries. Before local confirmed cases, the focuses of reporting of newspaper articles are: characteristics of ...
Electronics
The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions o...
CO.ME.T.A. - covid-19 media textual analysis. A dashboard for media monitoring
2020
The focus of this paper is to trace how mass media, particularly newspapers, have addressed the issues about the containment of contagion or the explanation of epidemiological evolution. We propose an interactive dashboard: CO.ME.T.A.. During crises it is important to shape the best communication strategies in order to respond to critical situations. In this regard, it is important to monitor the information that mass media and social platforms convey. The dashboard allows to explore the mining of contents extracted and study the lexical structure that links the main discussion topics. The dashboard merges together four methods: text mining, sentiment analysis, textual network analysis and latent topic models. Results obtained on a subset of documents show not only a health-related semantic dimension, but it also extends to social-economic dimensions.
Heliyon, 2021
Background: The emergence of COVID-19 pandemic has not only shaken the global health sector, but also almost every other sector, including economic and education sectors. Newspapers are performing a significant role by featuring the news of COVID-19 from its very onset. The temporal fluctuation of COVID-19 related key themes presented in newspaper articles and the findings obtained from them could offer an effective lesson in dealing with future epidemics and pandemics. Aim and method: This paper intends to develop a pandemic management framework through an automated content analysis of local newspaper coverage of COVID-19 pandemic in Bangladesh. To fulfill the aim, 7,209 newspaper articles are assembled and analyzed from three popular local newspapers named "bdnews24.com", "New Age", and "Prothom Alo English" over the period from January 1, 2020 to October 31, 2020. Results: Twelve key topics are identified: origin and outbreak of COVID-19, response of healthcare system, impact on economy, impact on lifestyle, government assistance to the crisis, regular updates, expert opinions, pharmaceutical measures, non-pharmaceutical measures, updates on vaccines, testing facilities, and local unusual activities within the system. Based on the identified topics, their timeline of discussion, and information flow in each topic, a four-stage pandemic management framework is developed for epidemic and pandemic management in future. The stages are preparedness, response, recovery, and mitigation. Conclusion: This research would provide insights into stage-wise response to any biological hazard and contribute ideas to endure future outbreaks.
Journalism and Media
The COVID-19 pandemic disrupted societies all over the world. In an interconnected and digital global society, social media was the platform not only to convey information and recommendations but also to discuss the pandemic and its consequences. Focusing on the phase of stabilization during the first wave of the pandemic in Western countries, this work analyses the conversation around it through tweets in English. For that purpose, the authors have studied who the most active and influential accounts were, identified the most frequent words in the sample, conducted topic modelling, and researched the predominant sentiments. It was observed that the conversation followed two main lines: a more political and controversial one, which can be exemplified by the relevant presence of former US President Donald Trump, and a more informational one, mostly concerning recommendations to fight the virus, represented by the World Health Organization. In general, sentiments were predominantly ne...
Journal of Information Technologies, 2022
Social media data can provide a general idea of people's response towards the COVID-19 outbreak and its reflections, but it cannot be as objective as the news articles as a source of information. They are valuable sources of data for natural language processing research as they can reveal various paradigms about different phenomena related to the pandemic. This study uses a news collection spanning nine months from 2019 to 2020, containing COVID-19 related articles from various organizations around the world. The investigation conducted on the collection aims at revealing the repercussions of the pandemic at multiple levels. The first investigation discloses the most mentioned problems covered during the pandemic using statistics. Meanwhile, the second investigation utilizes machine learning to determine the most prevalent topics present within the articles to provide a better picture of the pandemic-induced issues. The results show that the economy was among the most prevalent problems. The third investigation constructs lexical networks from the articles, and reveals how every problem is related through nodes and weighted connections. The findings exhibit the need for more research using machine learning and natural language processing techniques on similar data collections to unveil the full repercussions of the pandemic.