🎯 NarrativeScope - Social Media Narrative Intelligence Platform

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📋 Table of Contents

• Overview
• Features
• System Architecture
• Tech Stack
• Project Structure
• Installation & Setup
• API Documentation
• Frontend Features
• AI & Backend Logic
• Dashboard Screenshots
• Deployment
• Demo Video
• Contributing

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📱 Overview

NarrativeScope is an advanced social media narrative intelligence platform that analyzes digital conversations, tracks influence operations, and visualizes community sentiment across Reddit data. It combines semantic search, RAG (Retrieval-Augmented Generation), network analysis, and AI-powered insights to provide deep narrative understanding.

Key Capabilities:

• 🔍 Semantic search across millions of posts
• 💬 AI-powered intelligent conversational analysis
• 📊 Real-time time series analytics
• 🕸️ Network graph visualization of narrative connections
• 🤖 Topic clustering and detection
• 📈 Advanced sentiment and trend analysis

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✨ Features

🔭 Core Intelligence Features

Feature	Description
Semantic Search	Find narratives and discussions using natural language queries
RAG Chat Interface	Ask questions and get AI-synthesized answers grounded in source data
Network Analysis	Visualize author connections and narrative propagation patterns
Time Series Analysis	Track narrative evolution and sentiment over time
Topic Clustering	Discover and analyze emerging topics and narrative themes
Advanced Analytics	Comprehensive metrics on engagement, reach, and influence

User Experience

• ✅ Responsive React dashboard with Tailwind CSS styling
• ✅ Interactive visualizations with D3.js and Recharts
• ✅ Real-time data updates
• ✅ Intuitive navigation and filtering
• ✅ Export-ready analytics reports

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System Architecture

System Flow Diagram

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🛠️ Tech Stack

Backend

Framework:     FastAPI 0.115.0 + Uvicorn
Language:      Python 3.9+
LLM:          Groq (llama-3.3-70b-versatile)
Embeddings:   Sentence-Transformers
Vector DB:    ChromaDB
Search:       BM25 + Semantic Search
Graph:        NetworkX + python-louvain
Clustering:   HDBSCAN + UMAP
Data:         Pandas + DuckDB

Frontend

Framework:    React 19.2.4
Build Tool:   Vite 8.0.4
Styling:      Tailwind CSS 4.2
Routing:      React Router 7.14
State:        Zustand 5.0
HTTP:         Axios 1.14
Viz:          D3.js 7.9 + Recharts 3.8
Icons:        Lucide React 1.7

Infrastructure

API Protocol:  REST (HTTP/HTTPS)
Deployment:   Vercel (Frontend) + Server (Backend)
CORS:         Enabled for all origins
Database:     ChromaDB + PostgreSQL

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📁 Project Structure

Simppl/
├── 📂 backend/                          # FastAPI Backend
│   ├── main.py                          # App entry point
│   │
│   ├── 📂 routes/                       # API endpoints
│   │   ├── chat.py                      # Chat/RAG endpoint
│   │   ├── search.py                    # Semantic search endpoint
│   │   ├── timeseries.py                # Time series analytics
│   │   ├── network.py                   # Network graph endpoint
│   │   ├── clusters.py                  # Topic clustering endpoint
│   │   ├── posts.py                     # Post details endpoint
│   │   └── analytics.py                 # Advanced analytics endpoint
│   │
│   └── 📂 service/                      # Business logic
│       ├── rag_service.py               # RAG with LLM
│       ├── search_service.py            # Semantic search logic
│       └── analytics_service.py         # Analytics processing
│
├── 📂 frontend/                         # React Frontend
│   ├── package.json                     # Dependencies
│   ├── vite.config.js                   # Vite configuration
│   ├── tailwind.config.js               # Tailwind config
│   │
│   ├── 📂 src/
│   │   ├── main.jsx                     # React entry point
│   │   ├── App.jsx                      # Main app component
│   │   │
│   │   ├── 📂 pages/                    # Page components
│   │   │   ├── ChatPage.jsx             # Chat/RAG interface
│   │   │   ├── SearchPage.jsx           # Search results
│   │   │   ├── TimeSeriesPage.jsx       # Time series charts
│   │   │   ├── NetworkPage.jsx          # Network visualization
│   │   │   ├── ClustersPage.jsx         # Topic clusters
│   │   │   └── AnalysisPage.jsx         # Analytics dashboard
│   │   │
│   │   ├── 📂 components/               # Reusable components
│   │   │   ├── 📂 layout/
│   │   │   │   ├── Sidebar.jsx
│   │   │   │   └── PageShell.jsx
│   │   │   │
│   │   │   └── 📂 ui/
│   │   │       └── index.jsx
│   │   │
│   │   ├── 📂 api/                      # API client
│   │   │   └── client.js                # Axios instance
│   │   │
│   │   ├── 📂 hooks/                    # Custom React hooks
│   │   │   └── useFetch.js              # Data fetching hook
│   │   │
│   │   ├── 📂 store/                    # Global state
│   │   │   └── appStore.js              # Zustand store
│   │   │
│   │   ├── 📂 styles/
│   │   │   └── index.css                # Global styles
│   │   │
│   │   └── index.css                    # Tailwind imports
│
├── 📂 Data/                             # Data storage
│   ├── posts.parquet                    # Post dataset
│   ├── topics_data.json                 # Topic metadata
│   ├── network_graph.json               # Graph structure
│   │
│   └── 📂 chroma_db/                    # Vector database
│       └── embeddings/                  # Stored embeddings
│
├── 📂 Scripts/                          # Data processing
│   ├── ingest.py                        # Data ingestion
│   ├── embed_all_hf.py                  # Generate embeddings
│   ├── build_graph.py                   # Build network graphs
│   ├── train_topics.py                  # Topic modeling
│   └── precompute_topics.py             # Precompute topics
│
├── 📂 Analysis/                         # Notebooks
│   └── main.ipynb                       # Analysis notebook
│
├── 📂 docs/                             # Documentation
│   └── API.md                           # API documentation
│
├── requirements.txt                     # Python dependencies
├── .env                                 # Environment variables
├── .gitignore                           # Git config
└── README.md                            # This file

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🚀 Installation & Setup

Prerequisites

• Python 3.9+
• Node.js 18+

Backend Setup

# 1. Clone repository
git clone <repository-url>
cd Simppl

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Setup environment variables
cp .env.example .env
# Edit .env with your API keys:
# GROQ_API_KEY=your_groq_key
# HUGGINGFACE_API_KEY=your_hf_key

# 5. Prepare data (optional - if starting fresh)
python Scripts/ingest.py                 # Ingest posts
python Scripts/embed_all_hf.py          # Generate embeddings
python Scripts/build_graph.py            # Build network graphs
python Scripts/train_topics.py           # Train topic models

# 6. Start backend server
uvicorn backend.main:app --reload --host 0.0.0.0 --port 8000
# Backend will be available at: http://localhost:8000
# API docs: http://localhost:8000/docs

Frontend Setup

# 1. Navigate to frontend directory
cd frontend

# 2. Install dependencies
npm install

# 3. Create .env file
echo "VITE_API_BASE_URL=http://localhost:8000" > .env

# 4. Start development server
npm run dev
# Frontend will be available at: http://localhost:5173

# 5. Build for production
npm run build

# 6. Preview production build
npm run preview

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📡 API Documentation

Base URL

https://simppl-reasearch.vercel.app//api/v1

Health Check

GET /health

Chat / RAG Endpoint

POST /chat/message
Content-Type: application/json

{
  "message": "What are the main narratives about AI safety?"
}

Response:
{
  "reply": "Based on the retrieved data...",
  "sources": [
    {
      "author": "username",
      "score": 450,
      "domain": "reddit.com"
    }
  ]
}

Semantic Search

POST /search/semantic
Content-Type: application/json

{
  "query": "blockchain technology discussion",
  "top_k": 10
}

Response:
{
  "results": [
    {
      "document": "Post content...",
      "metadata": {
        "author": "user123",
        "score": 300,
        "timestamp": "2024-01-15"
      }
    }
  ],
  "total": 10
}

Time Series Analytics

GET /timeseries/narrative-trend?topic=AI&days=30

Network Graph

GET /network/graph?limit=100

Topic Clusters

GET /clusters/topics?top_n=20

Post Details

GET /posts/{post_id}

Advanced Analytics

GET /analytics/dashboard?date_range=30days

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🖥️ Frontend Features

Pages & Components

1. Chat Page (ChatPage.jsx)

• Natural language query interface
• RAG-powered responses with citations
• Source attribution and credibility metrics
• Multi-turn conversation support

2. Search Page (SearchPage.jsx)

• Semantic search across all posts
• Filter by author, date, score
• Result preview and detailed view
• Relevance scoring

3. Time Series Page (TimeSeriesPage.jsx)

• Narrative trend visualization
• Engagement metrics over time
• Peak detection and anomalies
• Recharts-powered interactive charts

4. Network Page (NetworkPage.jsx)

• Author connection visualization
• Community detection
• Influence measurement
• D3.js force-directed graphs

5. Clusters Page (ClustersPage.jsx)

• Automatic topic detection
• Cluster composition analysis
• Topic evolution tracking
• Community sentiment

6. Analysis Page (AnalysisPage.jsx)

• Comprehensive dashboard
• Multi-metric KPIs
• Export capabilities
• Custom date ranges

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🤖 AI & Backend Logic

RAG (Retrieval-Augmented Generation) Service

# Location: backend/service/rag_service.py

SYSTEM_PROMPT = """
You are the Lead Intelligence Analyst for NarrativeScope...
- STRICT GROUNDING: Only use provided context data
- MANDATORY CITATIONS: Every claim must be cited [Author: name | Score: X]
- NARRATIVE FORMAT: Write flowing analytical paragraphs
- HANDLING MISSING DATA: State when insufficient data exists
- ANALYTICAL TONE: Like an investigative journalist
"""

Process Flow:
1. User Query → Semantic Search (retrieve top-8 relevant posts)
2. Context Building → Format posts with metadata
3. LLM Call → Groq llama-3.3-70b-versatile (with system prompt)
4. Response → Return answer with sources

Search Service Architecture

User Query
    ↓
[Semantic Embedding] - Sentence-Transformers
    ↓
ChromaDB Vector Store (similarity search)
    ↓
BM25 Ranking (keyword relevance)
    ↓
Combined Results (semantic + lexical)
    ↓
Ranked Top-K Posts

Topic Clustering Pipeline

Data Ingestion
    ↓
Text Preprocessing (cleaning, normalization)
    ↓
Embedding Generation (Sentence-Transformers)
    ↓
UMAP Dimensionality Reduction
    ↓
HDBSCAN Clustering
    ↓
Topic Extraction & Labeling
    ↓
Output: Topic assignments + metadata

Network Analysis

Posts Data
    ↓
Extract Author Mentions
    ↓
Build Interaction Graph (NetworkX)
    ↓
Community Detection (python-louvain)
    ↓
Calculate Centrality Metrics
    ↓
Output: Network structure + influence scores

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📸 Dashboard Screenshots

1. Time Series Interface

2. Search Results

3. Network Visualization

4. Chat Analytics

5. Topic Clusters

6. Analysis Dashboard

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🌐 Deployment

Live Application

🔗 Website: https://simppl-reasearch.vercel.app/

Frontend Deployment

• Hosting: Vercel / Render
• Framework: React 19 + Vite
• Build: npm run build
• Deploy: Automatic via git push

Environment Variables

Backend (.env)

GROQ_API_KEY=gsk_xxxxx
HUGGINGFACE_API_KEY=hf_xxxxx

Frontend (.env)

VITE_API_BASE_URL=https://your-backend-api.com
VITE_ENV=production

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🎬 Demo Video

📺 Project Walkthrough & Explanation

🎥 Watch the complete project demo: Watch explantion

Video Contents:

• System overview and architecture
• Live demonstration of all features
• RAG in action - asking intelligent questions
• Network visualization explained
• Time series trend analysis
• Topic clustering results
• Backend API walkthrough
• Deployment process

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📊 Performance Metrics

Metric	Value
Search Latency	< 500ms (semantic)
RAG Response Time	< 3s (with LLM)
Embedding Generation	~100 posts/sec
Network Graph Render	< 2s (1000 nodes)
Concurrent Users	100+

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🔧 Configuration

Search Parameters

# Semantic search
top_k = 10              # Number of results
similarity_threshold = 0.5  # Min relevance score

# BM25 ranking
bm25_k1 = 1.5          # Term frequency saturation
bm25_b = 0.75          # Length normalization

Clustering

hdbscan_min_cluster_size = 5
umap_n_neighbors = 15
umap_min_dist = 0.1

RAG Model

llm_model = "llama-3.3-70b-versatile"
llm_temperature = 0.7
context_window = 4000 tokens
top_k_context = 8 posts

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Development Guidelines

• Follow PEP 8 for Python code
• Use ES6+ for JavaScript
• Add tests for new features
• Update documentation
• Keep commits atomic and descriptive

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👨‍💻 Author

Built with ❤️ for narrative intelligence & social media analysis

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📚 Resources

• FastAPI Documentation
• React Documentation
• Groq API Docs
• ChromaDB Docs
• D3.js Documentation

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📧 Support

For questions, issues, or suggestions:

• 📤 Email: [kukadiyarishi895@gmail.com]

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Last Updated: April 2024 | Version 1.0.0