Comparative Analysis of Topics Covered by False and True News in the Context of the COVID-19 Pandemic.
The COVID-19 pandemic has been an urgent topic of discussion in various media and social networks for a couple of years already. The lack of research and the rapid spread of the virus around the world only contribute to an increase in media interest in this topic, but these same reasons contribute to the emergence of many fake-news headlines and conspiracy theories. The article presents a study of the media coverage of the COVID-19 pandemic and the language used in the framework of fake news and its comparison with real news. The concept of ‘fake news’ does not have a single definition, but within the framework of this study, intentionally unreliable media reports, including propaganda, would be considered as such. Not only the spread of unreliable information by fake news but the undermined trust in news and media institutions are highlighted in the literature as the main alarming factors. With the help of network analysis, the connections between the words most intensively used in fake and real news headlines were identified. An analysis of correlations between the most frequently mentioned words in the headlines was carried out, based on which networks were built to analyze the main topics of news related to COVID-19. The study was conducted in an exploratory format and can be used as a basis for a deeper analysis of fake news related to the COVID-19 pandemic. In order to compare what drives words to form new phrases in both type of news headlines, the ERGM models were implemented with usage of BING sentiment variable and two structural network variables.