Title: Bot Network Detection & Mass Blocking System for X (Twitter) – Python / Data Engineering Expert Needed Project Overview: I am looking to hire an experienced developer / data engineer to help identify and neutralize a large-scale bot network (~5,000–50,000 accounts) on X (formerly Twitter). The goal is NOT manual reporting, but building a semi-automated system that can: Detect bot clusters starting from a seed list (50–500 known bot accounts) Expand the network to identify thousands of related bot accounts Identify controller / hub accounts coordinating these bots Generate structured outputs for mass blocking and reporting Scope of Work: Data Collection Use X API or scraping tools to extract: Followers / following data Tweet activity Retweet / reply relationships Account metadata (creation date, bio, etc.) Bot Detection System Build logic to detect bots based on: Account age patterns Username patterns Low follower / high following ratio Duplicate / near-duplicate tweet content Coordinated activity (timing patterns) Network Expansion Start from seed bots and expand to: Common follow targets Shared retweet sources Clustered communities Graph Analysis Build a graph model of accounts Identify: High-centrality nodes (controllers) Dense bot clusters Optional: visualization (Gephi or similar) Output Deliverables CSV/JSON of: Identified bot accounts Controller / hub accounts Optional: Python script to auto-block accounts via API Report-ready evidence for platform submission Technical Requirements (Must-Have): Strong Python experience Experience with APIs and/or scraping Familiarity with: Network analysis (networkx) Data handling (pandas) Experience with one or more: Twarc snscrape Understanding of bot detection / spam patterns Nice to Have: Experience working with social media data (X/Twitter preferred) Knowledge of graph visualization tools like Gephi Experience detecting coordinated inauthentic behavior Basic ML knowledge (for clustering / classification) Deliverables: Working Python scripts / pipeline Bot detection logic (well documented) List of identified bots + controllers Instructions to run the system Timeline: Initial prototype: 3–5 days Full system: 7–10 days Budget: Open to proposals (fixed or milestone-based) Important Notes: The goal is efficient detection and mitigation, not violating platform policies Solution should be scalable (5k → 50k+ accounts) Preference for candidates who have worked on similar anti-spam / OSINT / social graph problems To Apply: Please include: Relevant past work (especially similar projects) Tools / approach you would use Estimated timeline Any ideas to improve detection accuracy