InfoSys Cambridge Hackathon: Multi-Agent Fact-Checking for Socia…

What is InfoSys Cambridge Hackathon: Multi-Agent Fact-Checking for Social Media?

FactCheckBot is a multi-agent system built using the Model Context Protocol (MCP) that identifies, verifies, and responds to factual claims on social media platforms like Twitter (X). It decomposes fact-checking into modular agents responsible for specific reasoning steps, designed to combat misinformation with speed, context, and transparency.

How to use InfoSys Cambridge Hackathon: Multi-Agent Fact-Checking for Social Media?

The system takes a tweet or thread as input and runs a workflow of six agents sequentially: ClaimExtractorAgent, RetrieverAgent, VerifierAgent, CrowdSignalAgent, ExplainerAgent, and PosterAgent. The README does not specify installation or configuration steps beyond the agent flow.

Key features of InfoSys Cambridge Hackathon: Multi-Agent Fact-Checking for Social Media

• Six modular agents, each handling a distinct fact-checking task
• Uses Model Context Protocol for shared memory and agent coordination
• Falls back on crowdsourced consensus for breaking or unverified events
• Generates explainable verdicts with sources and reasoning
• Supports automatic reply posting to original tweets

Use cases of InfoSys Cambridge Hackathon: Multi-Agent Fact-Checking for Social Media

• Verifying breaking news claims before major outlets report them
• Detecting and correcting misinformation about current events or science
• Providing fallback credibility scores when traditional sources are unavailable
• Publishing structured, human-readable replies automatically to original tweets

FAQ from InfoSys Cambridge Hackathon: Multi-Agent Fact-Checking for Social Media

What platforms does FactCheckBot support?

The system is designed for social media platforms like Twitter (X). It extracts claims from tweets or threads and posts replies.

How does the system handle claims that can't be verified through traditional sources?

It uses CrowdSignalAgent to search for geolocated, independent social media posts within a time window, evaluate consensus, and provide a fallback credibility score.

What sources does the RetrieverAgent search?

It searches verified sources such as Wikipedia, arXiv, news APIs, and others mentioned in the README.

How does the system produce explainable verdicts?

The ExplainerAgent summarizes the verdict in human-readable language with reasoning, and the PosterAgent includes sources and explanations in the reply.

What is the role of the Model Context Protocol (MCP)?

MCP defines a shared memory structure that allows dynamic coordination and context-aware reasoning across all agents.