🛰️ RumorRadar : Real-Time Misinformation Detection System

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A real-time misinformation monitoring and alerting system built using Kafka, Python, and NLP. Analyzes streaming text data to detect potential rumors, emotionally charged narratives, and emerging misinformation trends with explainable alerts.

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⚠️ Project Status

🟡 Actively under development || REBUILDING Read Disclaimer

Some features are limited due to API rate limits and compute constraints. See Limitations for details.

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🚀 Features

✅ Real-Time Message Processing

• Kafka-based streaming architecture
• Handles high-throughput message ingestion
• Designed to scale horizontally

🧠 NLP-Powered Analysis

• Sentiment analysis for emotional tone detection
• Keyword & pattern-based filtering
• Early-stage misinformation signal detection

📈 Trend & Risk Detection

• Detects sudden spikes in sensitive keywords
• Flags emotionally charged or fear-inducing content
• Assigns risk levels to messages

🚨 Alerting System

• Real-time alerts for suspicious content
• Desktop notifications
• Console-based live monitoring

🔍 Explainable Output

Each alert includes:

• ✅ Triggered keywords
• ✅ Sentiment score
• ✅ Reason for flagging
• ✅ Risk classification

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🧩 System Architecture

┌──────────────┐
│ Data Source  │
└──────┬───────┘
       │
       ▼
┌──────────────────┐
│ Kafka Producer   │
└──────┬───────────┘
       │
       ▼
┌──────────────────┐
│ Kafka Consumer   │
└──────┬───────────┘
       │
       ▼
┌──────────────────────────┐
│ NLP + Risk Analysis      │
│ Engine                   │
└──────┬───────────────────┘
       │
       ▼
┌──────────────────┐
│ Alert System     │
└──────┬───────────┘
       │
       ▼
┌──────────────────────────┐
│ Live Monitoring /        │
│ Dashboard                │
└──────────────────────────┘

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🛠️ Tech Stack

Component	Technology
Backend	Python
Streaming	Apache Kafka
NLP	TextBlob (currently)
Alerts	Plyer + system notifications
Architecture	Event-driven, scalable
Future Scope	ML-based classification, dashboard UI

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📊 Current Capabilities

Feature	Status
Kafka-based streaming	✅ Complete
Keyword detection	✅ Complete
Sentiment analysis	✅ Complete
Alert system	✅ Complete
Risk scoring	⚙️ In progress
Trend detection	⚙️ In progress
Dashboard UI	🛠 Planned
ML-based rumor detection	🛠 Planned

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▶️ Getting Started

Prerequisites

• Python 3.8+
• Apache Kafka (running locally or remotely)
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/biv720/rumorradar.git
   cd rumorradar

2. Install dependencies

   pip install -r requirements.txt

3. Configure Kafka

   • Start Zookeeper:

     bin/zookeeper-server-start.sh config/zookeeper.properties

   • Start Kafka:

     bin/kafka-server-start.sh config/server.properties

4. Run the system

   python main.py

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🧪 Why This Project Exists

Misinformation spreads faster than verification.

RumorRadar aims to:

• ✅ Detect rumor formation early
• ✅ Provide explainable alerts
• ✅ Assist in analyzing information flow
• ✅ Act as a foundation for responsible AI-based monitoring tools

This project is built with scalability, transparency, and ethical AI principles in mind.

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⚠️ Important Note (Limitations)

This project is currently under active development.

Due to:

• API rate limits
• Compute constraints
• Free-tier infrastructure

Some features (such as large-scale message ingestion, advanced ML inference, and long-term trend analysis) are intentionally limited in the current version.

These constraints are documented and will be addressed in future iterations.

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🧭 Roadmap

• Add trend-based rumor scoring
• Implement message clustering
• Add web-based dashboard
• Improve NLP with transformer models (BERT/RoBERTa)
• Visualize misinformation spread patterns
• Deploy scalable cloud version (AWS/GCP)
• Integrate graph-based analysis
• Add multi-language support

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🤝 Contributing

Contributions are welcome! This project is open to improvements and new ideas.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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👨‍💻 Author

Bivraj A Computer Science Student | AI & Design Enthusiast

Focused on:

• AI-driven systems
• Real-time data processing
• Ethical and explainable AI
• Scalable backend architectures

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📌 Disclaimer

Current uploaded files are very old version, the v2 files are corrupt and is being worked on.

This project is intended for educational and research purposes only.

⚠️ It does not claim to identify factual truth and should not be used as a sole decision-making system for content moderation or fact-checking.

The system provides signals and insights, but human judgment and verification remain essential.

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built with 🧠 for a more transparent information ecosystem

⭐ Star this repo | 🐛 Report Bug | ✨ Request Feature