Social Media Market Auditor

Project Overview

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I developed a high-throughput data pipeline designed to transform fragmented social media activity into structured competitive intelligence. The system automates the entire lifecycle of data: from proxy-based web scraping to AI-driven synthesis and reporting.

1. Robust Data Acquisition (Scraping Layer)

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To bypass the complexities of modern web scraping (rate-limiting and IP-blocking), the engine utilizes a sophisticated collection strategy:

• Proxy-Based Ingestion: Integrated with residential proxy networks to ensure high-reliability data extraction from major social platforms.
• Dynamic Headless Browsing: Uses automated browser environments to capture content rendered via JavaScript, ensuring no data loss compared to standard HTTP requests.

2. AI-Driven Synthesis & Sentiment Analysis

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Once the raw HTML/JSON is captured, it passes through a multi-stage refinement process:

• Contextual Summarization: Leverages the Gemini 2.0 Flash model to distill thousands of data points into a concise 360-degree overview.
• Sentiment Grading: Automatically categorizes public discourse into positive, negative, or neutral vectors, providing a quantitative look at qualitative data.
• Structure Formatting: Translates unstructured social “noise” into structured Markdown or JSON reports ready for executive review.

3. Scalable Orchestration

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The engine is built on an event-driven architecture that allows for:

• On-Demand Audits: Can be triggered via webhook to generate a report for a specific brand or keyword in real-time.
• Asynchronous Processing: Handles the “request-wait-process” cycle of web scraping without blocking the main execution thread, allowing for multiple reports to be generated simultaneously.

Technical Architecture Summary

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