Shady Elbassuoni
Data Centric Fake News Detection During Armed Conflicts
Armed conflicts continue to be a major global issue, causing widespread human suffering, displacement, and economic instability. Fake news can further fuel armed conflicts by manipulating public perception, inciting violence, and undermining efforts towards resolution. In this talk, I will argue why a one-size-fits-all approach for fake news detection is not adequate during armed conflicts. I will then present a data-centric approach for fake news detection, focusing on the Syrian civil war as a case study. The approach utilizes a knowledge graph of conflict casualties to construct a fake news dataset, and then employs meta-learning to automatically detect fake news. I will present experimental results that demonstrate the effectiveness of this approach compared to various baselines, and will conclude with a few potential avenues for future research.