A Multimodal Ensemble Machine Learning Approach to COVID-19 Misinformation Detection in Twitter

Department of Computer Science, University of Kashmir, Srinagar 190006, India

^* Rayees Ahmad Dar: rayees.csscholar@kashmiruniversity.net

Abstract

The emergence of social media platforms has unquestionably altered the manner in which people ingest information, with tweets now functioning as the primary source for news and other types of content. However, the proliferation of false news on these platforms has become a major concern, as it poses a severe threat to both individuals and society as a whole. Consequently, it is crucial to develop efficient methods for detecting false news in tweets. This study presents a novel hybrid approach that integrates the textual content of tweets with auxiliary features to detect false news. Our approach uses a pre-trained transformer-based language model, COVID-twitter-BERT to encode the text content of tweets into a dense representation that captures their meaning. The auxiliary features, such as sentiment score, credibility score, engagement score, average retweet count, average favourite count, and average followers of followers, are fed into a stacking classifier-based model to predict the trustworthiness score of the tweet. By combining the predictions of both models, we demonstrate that our approach outperforms baseline methods, emphasising the significance of utilising both text content and auxiliary features for Twitter false news detection. Our research considerably advances the field of detecting false news by demonstrating the effectiveness of integrating transformer-based language models and machine learning models for this task. Our findings provide valuable insights for improving the detection of false news on social media.