Team ForensIQ proudly qualified as college finalists for Smart India Hackathon 2024, successfully clearing all internal rounds among 400+ competing teams. We tackled problem statement SIH1743 and developed a fully functional prototype: S.M.A.R.T — Social Media Automatic Review Tool.
The traditional manual approach of examining social media accounts is not only time-consuming but also prone to human error. Our proposed solution is an automated tool designed to parse, capture, and document social media data efficiently, ensuring thoroughness and accuracy in the investigative process.
- Cross-platform automation for platforms like Facebook, Instagram, WhatsApp, Telegram, Google, and Twitter.
- Efficient capturing of posts, messages, timelines, followers/friends, etc.
- Centralized report generation with extracted insights.
- AI-assisted parsing using LLM models for summarizing and categorizing content.
Prototype_Video_Explaination.mp4
Complete Documentation: SIH_PPT
- Web Automation - Selenium
- Data Parsing - Ollama's LLAMA3.1
- API Development - FastAPI, Python
- Frontend - HTML, CSS, JS, Bootstrap
Note
This project is currently in the prototype phase. It may not be fully stable or compatible across all systems.
# Clone the repository
git clone https://github.com/Adamya-Gupta/SMART.git
cd SMARTImportant
Additional packages may be required depending on your environment.
pip install fastapi selenium uvicorn # Start FastAPI Server
uvicorn main:app --reloadOpen your browser and navigate to:
http://127.0.0.1:8000
SMART/
├── static/
├── templates/
├── main.py
├── instagram_script.py
├── telegram_script.py
├── whatsapp_script.py
└── data.json
The Social Media Automatic Review Tool (S.M.A.R.T) demonstrates the potential of automation and AI in modern digital forensics. By streamlining the review process of social media data, it reduces manual effort, eliminates oversight, and enhances the reliability of cybercrime investigations. As cyber threats grow in complexity and scale, tools like S.M.A.R.T are crucial in equipping investigators with intelligent, scalable, and efficient solutions.


