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Project Opulus

Social Media Algorithm Simulator

Technologies

TypeScript Node.js React D3.js Graph Algorithms Data Structures WebSocket

Status Active Development

Category Social Network Analysis & Education

Project Overview

Project Opulus is an interactive simulation platform that visualizes how social media algorithms shape information distribution. By modeling user networks with thousands of simulated nodes, it demonstrates the impact of algorithmic decisions on content visibility, user engagement, and information diversity.

85%

Prediction Accuracy

50K+

Simulated Interactions

10K+

Network Nodes

Key Features

📊

Algorithm Visualization

Real-time visualization of content propagation through social networks using D3.js with animated transitions

🎛️

Parameter Tuning

Interactive interface for experimenting with algorithm weights, decay rates, and engagement thresholds

🔍

Filter Bubble Analysis

Demonstrates echo chamber effects and measures content diversity metrics across user demographics

📈

A/B Testing Framework

Side-by-side comparison of different algorithmic strategies with statistical significance testing

🎓

Educational Dashboard

Guided tutorials and interactive demonstrations explaining algorithm mechanics for students and researchers

🌐

Multi-User Collaboration

WebSocket connections enabling real-time collaborative experimentation and shared simulations

Technical Implementation

$ Architected interactive simulation platform visualizing social media content distribution algorithms using TypeScript

$ Implemented multiple algorithm models including engagement-based ranking, chronological feeds, and hybrid recommendation approaches

$ Developed graph-based data structures representing user networks with 10K+ simulated nodes and content propagation patterns

$ Created real-time visualization engine using D3.js showing how content spreads through network connections with animated transitions

$ Built comprehensive parameter tuning interface allowing experimentation with algorithm weights, decay rates, and thresholds

$ Engineered metric tracking system measuring reach, engagement velocity, and algorithmic amplification effects

$ Developed user behavior simulation modeling different interaction patterns including lurkers, influencers, and casual users

$ Implemented filter bubble visualization demonstrating echo chamber effects and content diversity metrics

$ Created A/B testing framework comparing different algorithmic strategies side-by-side with statistical significance testing

$ Built educational dashboard explaining algorithm mechanics with interactive demonstrations and step-by-step walkthroughs

$ Developed content virality prediction model analyzing factors contributing to viral spread

$ Implemented network topology analysis tools identifying influential nodes and community structures

$ Created recommendation algorithm comparison including collaborative filtering, content-based, and hybrid approaches

$ Built time-series analysis showing how algorithm changes affect user engagement over time

$ Developed export functionality generating comprehensive reports with data visualizations and insights