Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
This addresses the problem of misinformation spread for social media users and platforms during crises, but it is incremental as it applies an existing ICA method to a new domain with a new dataset.
The paper tackles misinformation detection on social media during high-impact events like COVID-19 by proposing an Independent Component Analysis (ICA)-based method that jointly performs knowledge discovery and detection, achieving performance comparable to deep learning methods on a newly developed labeled Twitter dataset.
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.