Identification of Fake News using Fetch.ai Agent Technology - Aakash, Aakash; Ghosh, Subhajit (Journal of Neonatal Surgery, 2025)
The proliferation of fake news across digital platforms poses a significant threat to informed public discourse, societal trust, and democratic processes. Traditional centralized approaches to fake news detection often face limitations in scalability, transparency, and susceptibility to bias or censorship. This paper presents a novel decentralized approach for the identification of fake news, leveraging the capabilities of Fetch.ai agent technology and the ASI:One (Artificial Superintelligence) Web3 Large Language Model (LLM) . We propose a multi-agent system architecture deployed on the Fetch.ai blockchain, where autonomous agents collaborate to verify the veracity of news headlines. The core methodology involves a Fake News Detector Agent orchestrating a workflow that includes: receiving user requests, tasking a specialized Travily agent for autonomous web information retrieval from credible sources and fact-checking databases, and integrating with the ASI:One LLM for advanced, verifiable natural language processing tasks such as semantic analysis, claim extraction, and contradiction detection. All verification steps, agent interactions, and the final veracity assessment are immutably recorded on the Fetch.ai blockchain using Almanac smart contracts, ensuring end-to-end transparency and providing a verifiable audit trail. This decentralized framework enhances resistance to manipulation and preserves user privacy. Preliminary evaluations suggest the potential for high accuracy in distinguishing fake news from legitimate reporting, while the system’s inherent transparency offers significant advantages over opaque, centralized models. This research contributes a robust, scalable, and trustworthy technological solution to combat the spread of misinformation in the digital age, demonstrating the power of combining decentralized autonomous agents with verifiable Web3 AI