COVID-19 fake news on Twitter: A statistical and sentimental analysis

ABSTRACT

There have been increasing concerns about spreading COVID-19 fake news and misinformation from social media sites (SNSs) such as Facebook and Twitter, as they facilitate connection and communication on a large scale. Because of the massive amount of information transmitted through SNSs, manual verification of such information is impossible, prompting the development and implementation of automated methods for fake news identification, aka automatic fact-checking. Fake news creators employ a variety of aesthetic tactics to increase their success rates, one of which is to excite the readers' sentiment. Therefore, this research uses sentiment analysis to analyze whether sentimental and emotional words in SNSs content could explain the situations between the spreading of true and fake news. In this way, governments and platform providers could take action to help the general public identify fake news and misinformation and curb them at their source. This research also offers insights to the public on the importance and impacts of sentiment words in SNS content. © 2023, IGI Global. All rights reserved.