DSMIP – Distributed Social Media Intelligence Platform

Project Foundation

The Problem We Solved

01

Track Brand Presence

• Monitor trends & news
• Cover multiple platforms
• Track economic developments

02

System Gap

• No unified system
• Missing real-time collection
• Fragmented sources

03

Analysis Challenges

• Limited propagation analysis
• Slow trend detection
• Reduced effectiveness

Core Objectives

Unified Platform: Integrate social media & news data in real time

Holistic View: Brand performance across multiple platforms

Proactive Management: Early detection of trends & crises

Business Impact

Audience Segmentation: Targeted marketing & PR campaigns

Automation: Data collection, aggregation, & analysis

Data-Driven Decisions: Actionable insights for leadership

Execution Timeline

Project Phases & Deliverables

1

Investigation

Research & planning phase

Define Goals: Overall platform objectives & success criteria
Research Sources: Reddit, Telegram, Discord, GitHub
Map Capabilities: Align platform features to needs
Research Output: Academic paper on media intelligence

2

Development

Design, prototype & build pipelines

Design ETL: Data pipelines & application architecture
Implement Pipelines: High-volume data ingestion
Infrastructure: Integrate BigData4Biz platform
Deployment: Docker & CI/CD pipelines

3

Data & Analysis

Quality assurance & enrichment

Validate Quality: Data accuracy & completeness checks
Enrichment: Metadata & segmentation
AI Integration: Chat prompt engineering
Analytics Ready: Prepare for insights generation

4

Project Closing

Delivery & handoff

Documentation: Complete technical & user guides
Presentation: Results & achievements to stakeholders
Knowledge Transfer: Team training & support
Operations: Transition to prod support

Technology Stack

Tools, Platforms & Environment

Management Tools

Project Plan (PID)
Gantt Charts
Documentation

Development Stack

Docker
BigData4Biz
Python
GitLab

Communication

WhatsApp
Discord
Jitsi Video

Technical Challenges

GitHub API Pagination

Limited to 100 results per page with incomplete historical coverage

→ Recursive loop extracts complete dataset

Mixed Responses

Issues mixed with pull requests in API responses

→ Key-based filter isolates pure issues

Encoding Errors

Emoji & special characters cause parsing failures

→ UTF-8 normalization enforced on all writes

Features & Outcomes

Recursive Pagination

Complete historical data extraction across all pages

Issue Filtering

Pure issue data separated from pull request noise

Normalization Layer

Structured documents optimized for LLM processing

Key Outcomes & Impact

100%

Complete Coverage of historical GitHub issues

∞

High-Volume Ingestion without pagination limits

✓

Analytics Ready structured data for AI

Architecture & Flows

System Diagrams & Pipelines

System Architecture

Complete platform architecture showing data sources, ingestion pipelines, and analytics layers

GitHub Data Flow

GitHub API extraction, filtering, normalization, and analytics pipeline flow

DevOps & CI/CD Pipeline

Automated deployment, testing, and infrastructure management pipeline

Technical Expertise

Skills Demonstrated

GitHub Module

ETL Design API Integration Data Normalization UTF-8 Handling Recursive Pagination Error Handling Structured Validation Analytics Engineering

Platform Architecture

BigData4Biz Docker Python GitLab CI/CD System Design Data Pipeline Real-time Analytics AI Integration

Interested in learning more?

This GitHub ingestion module is just one component of the broader Distributed Social Media Intelligence Platform, engineered for real-time insights, crisis detection, and data-driven communication strategies.

Note: Source code is not publicly available, as this work was part of a university project in collaboration with Dibuco Company.

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