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Project Title: AURA – The Autonomous Unified Response Agent for Crisis Misinformation
Category: Agentic AI for Real-Time Crisis Misinformation Management

Problem Overview
During crises like pandemics, wars, or disasters, social media and online platforms overflow with contradictory, emotionally charged, and often false information. Traditional fact-checking is too slow to match the viral spread of misinformation, creating confusion, panic, and even physical harm.

Our Vision: AURA
AURA (Autonomous Unified Response Agent) is an agentic AI ecosystem designed to autonomously monitor, verify, and counter misinformation in real-time. Built on a multi-agent architecture, AURA coordinates autonomous agents, each with a specialized role, orchestrated by a central “Brain.”
Mission: detect misinformation early, verify it transparently, and educate the public proactively.

📄 The architecture and methodology are detailed in our research paper:
“AURA: An Autonomous Multi-Agent System for Crisis Misinformation Management”
— authored by Sumit Chourasia, Suhani Pandit, Aditya Jha, and Annanya Prasad.

System Overview: How AURA Works
AURA operates as a closed information loop connecting four primary modules:

1. Central Command — The Brain
   Role: Orchestrates all agents, detects trends, and directs verification.

2. Content Ingestion — The Eyes
   Role: Multi-modal agents scan and process text, video, and official data streams.

3. Verification Engine — The Knowledge Core
   Role: Evidence-based verification via debate mechanisms, RAG, and knowledge graphs.

4. Communication Interface — The Tongue
   Role: Creates audience-specific explanations and prebunking content.

The “Eyes”: Multi-Modal Scanning Agents
These agents form AURA’s sensory layer, continuously ingesting information:

1. Social Media Stream Monitor: Scans X, Facebook, TikTok then filters crisis content via NLP and geotags.
2. News & Blog Scanner: Extracts articles; performs sentiment and framing analysis.
3. Video & Audio Transcriber: Uses STT, OCR, and Vision AI to extract text and visuals from multimedia.
4. Government Source Monitor: Tracks press releases, advisories, and WHO/UN data as the “ground truth.”

Output: Structured data packets (text, metadata, timestamps, entities) → streamed via Kafka to the Brain.

The “Brain”: Central Orchestrator
Receives signals from the Eyes for trend detection, clustering, and orchestration:

1. Trend Detection Engine: Groups similar claims; detects viral falsehoods early.
2. Crisis Context Module: Uses domain knowledge (pandemics, elections, disasters) to interpret data.
3. Task Orchestrator: Assigns high-priority claims to the Knowledge Core, directs further evidence collection, and instructs Tongue on communication.
4. Data Flow: Eyes → Brain (clustering + prioritization) → Knowledge Core (verification) → Tongue (communication)

The “Knowledge Core”: Verification and Evidence Engine
AURA’s truth machine:

1. Dynamic Knowledge Graph (Neo4j): Continuously updated with verified facts.
2. Multi-Agent Debate Framework: Agents (supporting, opposing, skeptic, judge) debate claims using RAG.
3. Source Credibility Agent: Scores sources based on accuracy, bias, and transparency.

Process: Claim → Knowledge Graph lookup → Debate → Credibility scoring → Verdict + Evidence
Output: Verified claim (True/False/Misleading) + Confidence score + Evidence trace

The “Tongue”: Contextual Explanation & Public Interface
Converts verification results into human-readable insights:

1. RAG-based Explanation Generator: Produces grounded, evidence-linked summaries.
2. Audience-Specific LLMs: Tailored tone for citizens (simple), journalists (concise + citations), government (technical).
3. Gamified Prebunking Module: 90-second “misinformation defense” mini-game that reduced misinformation sharing intent by 28% (Cambridge University, 2022) — [Van der Linden et al., Nature Human Behaviour, 2022].

Outputs: Crisis dashboards, media fact-check reports, API alerts for government systems

Information Flow Summary:

• Ingestion: Eyes collect multi-modal data.
• Analysis: Brain clusters and prioritizes misinformation.
• Verification: Knowledge Core fact-checks via debate, graph search, and credibility scoring.
• Explanation: Tongue generates narratives and prebunking experiences.

Feedback Loop:

• User submissions and corrections retrain Brain.
• Knowledge Core updates graphs with new verified data.

Research Reference:
Chourasia, S., Pandit, S., Jha, A., & Prasad, A. “AURA: The Autonomous Unified Response Agent for Crisis Misinformation” . Read Full Paper -