Higher-PHQ users bring more mental-health, relational, late-night, and high-disclosure concerns to ChatGPT without higher professional redirection, and language prediction is too weak for screening (AUROC 0.591).
Gummadi, Animesh Mukherjee, Ingmar Weber, and Savvas Zannettou
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
LLM-based inference recovers user age, gender, and country from filtered ChatGPT logs at weighted F1 scores of 0.84-0.90, with median identification from the first 5% of history, driven by stereotype patterns.
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Depression Symptoms and Relational Patterns in 187k ChatGPT Histories
Higher-PHQ users bring more mental-health, relational, late-night, and high-disclosure concerns to ChatGPT without higher professional redirection, and language prediction is too weak for screening (AUROC 0.591).
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Inferential Privacy Leakage in Anonymized Conversational AI Logs
LLM-based inference recovers user age, gender, and country from filtered ChatGPT logs at weighted F1 scores of 0.84-0.90, with median identification from the first 5% of history, driven by stereotype patterns.