Exploring the surge of negativity during the COVID-19 pandemic: computational text and sentiment analysis across eight newsrooms’ tweets

Abstract

The rise of Twitter as a news platform has radically changed the way we access, consume, and share news. Twitter becomes an important hub to quickly and easily access accurate information in times of crisis such as COVID-19 and is frequently used in journalism practices. This study examines how the COVID-19 pandemic is covered by news agencies in the Twitter ecosystem, the weight of the news about the pandemic in tweets and the sentiment analysis of the news. Within the scope of the study, the tweets related to COVID-19 shared between 2020 and 2021 by eight news agencies (BBC World, Reuters, CNN, Associated Press, TRT World, AL Jazeera English, DW English, Euronews) that broadcast on a global scale and have a high number of followers on Twitter are analyzed by using text mining methods. Firstly, the frequently used words in tweets were obtained by using the text analysis technique n-gram. Secondly, the sentiment values of all the tweets and the words are computed and later classified into certain categories. Lexicon based sentiment dictionaries such as VADER and NRC utilized in the sentiment analysis process. Findings reveal that messages containing fear, anxiety, sadness, and negative polarity are prevalent in the news during the pandemic.

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