Social Media Suppression Audit

I need a data-savvy investigator to help me confirm whether social-media content is being deliberately down-ranked or hidden. My sole aim is to identify suppressed algorithms, with a particular emphasis on content-visibility suppression on major social platforms.

You will design and execute an evidence-based study: gather relevant data points (public APIs, scraped feeds, or previously archived datasets), apply statistical or machine-learning techniques to spot anomalous reach patterns, and document any rules, thresholds, or signals that appear to mute posts. I am open to your preferred toolkit—Python, R, SQL, or specialty OSINT platforms—as long as the workflow is reproducible.

What matters most is a clear, defensible methodology, transparent code, and a concise explanation of the findings that a non-technical stakeholder can understand. If you uncover heuristics behind the suppression, flag them; if the analysis proves inconclusive, explain why and suggest next steps.

Deliverables
• A brief research plan outlining data sources and metrics
• Executable scripts/notebooks and a README for replication
• A written report summarizing methodology, results, visualisations, and your interpretation

I will review the evidence against my own benchmarks for statistical significance, so precision and clarity are essential.