Tracking of Disinformation Sources: Examining Pages and URLs

Online social media (OSM) platforms have become hotbeds for various cybercrimes. Attackers often employ "shotgun" approaches, slightly altering their malicious content to evade detection and distributing it across multiple OSM channels to target more victims. This tactic is used to launch fake news, political propaganda, and social engineering attacks. To address these issues, we propose a novel framework that uses artificial intelligence (AI) to effectively trace the source of disinformation and detect content with abnormal similarities. Our approach not only analyzes the content itself but also focuses on the external uniform resource locators (URLs) associated with the content and the specific pages where the malicious content appears. The framework is designed to filter out suspicious content, external URLs, and channels based on a few verified instances of disinformation, using an adaptive approach that can quickly respond to evolving disinformation tactics. The framework was evaluated using a real dataset and revealed that certain channels on OSM platforms function as critical bridges between normal and disinformation clusters. Our study offers valuable insights for OSM platforms and administrators to proactively detect clusters of disinformation messages, external URLs, and channels with only a small number of manually verified samples. Additionally, it illuminates the role and dynamics of how disinformation evolves and spreads, which can aid in developing more effective strategies to combat disinformation on OSM platforms.