Abstract:
Fake News has become the curse of our time. Online social media networks provide a low-cost platform to facilitate information and fact sharing, but it fails to o er any quality control. As the number of people receiving their daily news through these platforms increases, it becomes a signi cant problem for the government and other organizations. Fake News articles leverage the multimedia content posted on the platforms and mislead the reader through fabricated image(s) or text (title and text body) accompanying it. Many organizations have started an initiative to provide de-bunked fake news, i.e., fact-checked and veri ed counterfeit news items oated on various social media platforms by human fact-checkers. Though this human intervention is a good start towards eradicating this evil, it can not be feasible at a larger scale providing human fact-checked information for every post on social media. The scalability of this human fact-checked information isn't the only issue, but the promptness of such accurate information becomes crucial in this digital age. To address this problem, we aim to analyze multimodal fake content from platforms supporting online journalism (including various social media platforms) to extract meaningful features better and design an all-inclusive early-stage Automated Fake News Detection System.
