Exploring COVID-19 Discourse: Analyzing Sentiments for Fake News Detection in Twitter Topics

Abstract

The COVID-19 pandemic generated extensive and far-reaching discussions on social media platforms, effectively becoming a primary source for people to access and disseminate information regarding the outbreak. These social media conversations possess the potential to shape public opinions, but they also carry the risk of spreading panic and misinformation during crises like the COVID-19 pandemic.Given this context, it becomes imperative to identify the prevailing topics under discussion on social media and gain insights into people's perceptions, opinions, and emotions by conducting sentiment analysis on user interactions concerning these topics. As a response to this challenge, this paper introduces a novel approach for conducting sentiment analysis that is sensitive to the topics under discussion with the aim of analysing online conversations on Twitter and identifying false information associated with COVID-19. To tackle this challenge, we have devised a semi-supervised methodology that combines false information detection with sentiment-aware topic modeling algorithm. Linguistic and sentiment features are initially extracted for subsequent analysis. Following this, a set of cutting-edge machine learning algorithms are employed to classify the COVID-19-related dataset. These algorithms are subsequently assessed using a range of metrics. The findings indicate that the incorporation of our model's fake news feature extraction resulted in a notable accuracy improvement, increasing it from 59.20% to 88.12%. and that the ensemble-based approaches excels, surpassing the other classifiers with an accuracy rate of 88.50% for XGBoost and 84% for AdaBoost.