Evaluating The Preliminary Models to Identify Fake News on COVID-19 Tweets

The spread propagation of fake news about COVID-19 can make it distressing to handle the pandemic situation. Identifying the fake and real news on social media needs to be done as quickly as possible to prevent chaos in the community and hampering the handling of COVID-19. In this study, we conducted some experiments to get a model that works well for classifying information into fake or real news using tweet data. We implemented two different ways to represent data to train machine learning classifier models, syntactic-based using Bag-of-Words and TF-IDF, and semantic-based using Word2Vec and FastText. We evaluated each model produced by the training process using two types of testing data. The results show that The Linear Support Vector Machine model using TF-IDF obtained the best F1-Score value in both testing data. The model obtained F1-Score 92.21% in Testing Data 1 and 93.33% in Testing Data 2.