Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models explores Integrates psychological theory with computational models to predict mental health from social media data.. Commercial viability score: 3/10 in Mental Health AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisEnhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing AgentsViability: 2.0
- Prior WorkA Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Large Language ModelsViability: 3.0
- Higher ViabilityExperiences Build Characters: The Linguistic Origins and Functional Impact of LLM PersonalityViability: 7.0
- Higher ViabilityTrustMH-Bench: A Comprehensive Benchmark for Evaluating the Trustworthiness of Large Language Models in Mental HealthViability: 6.0
- Higher Viability