CovFakeBot: a machine learning based chatbot using ensemble learning technique for COVID-19 fake news detection. (7th October 2022)

• Record Type: Journal Article
• Authors: Batra, Hunar; Palawat, Gunjan Kanwar; Gupta, Kanika; Bajaj, Deepali; Bharti, Urmil
• Is Part Of: International journal of artificial intelligence and soft computing. Volume 7:Number 3(2022)
• Page Start: 228
• Page End: 241
• Publication Date: 2022-10-07
• Subjects: COVID-19 -- fake news detection -- ensemble learning -- voting technique -- Twilio sandbox -- chatbot -- soft voting classifier -- natural language processing; Artificial intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-4950

Abstract

The outbreak of the SARS-Cov-2 virus epidemic has been followed by a flood of misleading information on social media which is impacting millions of people every day. For this, we developed CovFakeBot, a conversational bot based on machine learning models to distinguish between fake and real news. The bot also provides the confidence score for the prediction that helps to ascertain trustworthiness of the news. Our system uses the COVID-19 tweet dataset and is trained over well-established state-of-the-art machine learning models. It is further optimised using ensemble learning methods for better accuracy. Results are evaluated using the accuracy and F1-score. We observed that ensemble learning using soft voting outperformed thus; we claim it as the best fit model. CovFakeBot is using WhatsApp Business API with Twilio to achieve conversational user interface. CovFakeBot will help the public to easily classify news as real or fake.