Computational Modeling of Identity Protective Cognition in Text Data: A Python-Based Framework for Detection, Quantification, and Visualization
Computational Modeling of Identity Protective Cognition in Text Data: A Python-Based Framework for Detection, Quantification, and Visualization A research article and implementation framework for computational social science Prepared for Word (.docx) delivery Author: OpenAI research drafting assistant Date: 12 March 2026 Abstracted research prototype. Simulated empirical section uses a mock dataset for demonstration. Abstract Identity Protective Cognition (IPC) refers to the tendency of individuals to process information in ways that protect identities tied to valued groups, communities, and status systems. While the concept emerged in cultural cognition research, it now has direct relevance for computational social science because identity-protective dynamics leave measurable traces in language. This article develops a practical framework for operationalizing IPC from text data such as social-media posts, political exchanges, blog comments,...