Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19

TL;DR

This paper introduces an ICA-based data-driven approach to detect COVID-19 misinformation on social media, offering an interpretable alternative to deep learning methods during high impact events.

Contribution

It presents a novel application of Independent Component Analysis for misinformation detection, providing a transparent and effective solution compared to existing deep learning techniques.

Findings

ICA-based method effectively detects misinformation

Compared favorably with deep learning approaches

Developed a labeled COVID-19 Twitter dataset

Abstract

Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. While recent solutions that are based on machine learning have shown promise for the detection of misinformation, most widely used methods include approaches that rely on either handcrafted features that cannot be optimal for all scenarios, or those that are based on deep learning where the interpretation of the prediction results is not directly accessible. In this work, we propose a data-driven solution that is based on the ICA model, such that knowledge discovery and detection of misinformation are achieved jointly. To demonstrate the effectiveness of our method and compare its performance with deep learning methods, we developed a labeled COVID-19 Twitter dataset based on socio-linguistic criteria.