Recruiting study
Conversation Flow-Based Learning for Community Intelligence Extraction
Fine-tuning transformers on hierarchical Reddit threads to extract collective personality, burnout, and critical-issues profiles from a single model — no per-assessment retraining.
Status
Recruiting
Eligibility
Open to collaborators interested in NLP, computational social science, or organizational assessment
Time commitment
Async; reading drafts / discussing methodology
Contact
[Study Description] Dissertation work (Wright State CSE) presenting a conversation flow-based methodology that preserves threaded reply structure to train transformer models (GPT-2, SmolLM2-135M/360M) as computational proxies for community voice. A single model fine-tuned on r/Teachers discourse serves four structurally distinct instruments — MBTI, Big Five, Copenhagen Burnout Inventory, and LDA-based critical-issues identification — through a shared, assessment-agnostic pipeline. Cross-architecture convergence (unanimous ISFJ, preserved Big Five rank ordering, sub-point CBI agreement) and cross-community differentiation via Moral Foundations profiling (r/AskConservatives vs. r/democrats) provide validation evidence that extracted traits reflect authentic community characteristics rather than model artifacts.