COVID-19 fake news analytics from social media using topic modeling and clustering

ABSTRACT

Nowadays, Health misinformation and myths regarding various types of disease has spread on social media which terrified the public. During COVID-19 pandemic, misinformation and fake news outbreak increased as social media platforms play important role to enable people to view, search, and share the news as well as their point of view globally. Social media users might find difficulties in checking the validity of the news as they could not differentiate which one are the authorized news. Thus, it is too risky if people could easily be swayed by believing the news without validation. Therefore, the goal of this research is to classify the news related to COVID-19 using topic modeling and clustering. Latent Dirichlet Allocation is used for topic modeling of the fake and real news. This study can increase the awareness among social media users to reduce the risk of believing and sharing the misinformation especially during COVID-19 pandemic. © 2022 Elsevier Inc. All rights reserved.