AI For Real-Time Detection And Mitigation Of Fake News Via Multi-Source Verification

The drought of fake news in the cover of the digital age makes information quality and its influence by senses. To address this issue, this paper introduces an innovative process to counter the misinformation spread by designing a real-time detection and mitigation model which is based on an AI Environment. Existing solutions do postpublication fact-checking, but our proposed system performs cross-verification of news sources for extracting facts from publications, it observes language patterns and compares content with trusted databases, analyzing the reliability among them in real-time. We empirically show that this approach achieves a significant improvement in performance over the state-of-the-art on both the LIAR dataset and real-time social media analysis. Our results demonstrate that the system is able to attain 89% of accuracy on detecting fake news, with a processing latency lower than 1.5s per article. It provides a real-time scalable solution to detect and combat fake news, assisting in the continued fight to keep online information accurate