This computational research project investigates how contraceptive misinformation spreads and amplifies across digital platforms. Using natural language processing and AI techniques, it will: (1) analyze community-specific misinformation patterns across 8 social media platforms (Reddit, Twitter, TikTok, etc.) by examining rhetorical strategies and linguistic distortions in contraceptive discourse; (2) quantify algorithmic amplification biases through simulated AI agent experiments that measure how recommendation systems prioritize belief-consistent content for users with different psycholinguistic profiles; and (3) develop intervention-ready outputs including NLP classifiers for real-time misinformation detection, API modification specifications for platforms, and open-source toolkits for researchers and health departments.
The methodology employs transformer-based models (BERT), causal inference techniques, and Hawkes process modeling to track cross-platform misinformation propagation. By focusing on populations disproportionately impacted by contraceptive access barriers, the project prioritizes equity-informed scalable computational solutions. Every aspect of this work utilizes public API-compliant data, with validation against medical guidelines from FDA/WHO. The project aims to deliver actionable intelligence to help platforms, policymakers, and healthcare providers combat evolving contraceptive misinformation through algorithmic auditing and precision community-specific countermeasures.