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CUNY team uses LLMs for mental health change analysis

By PulseAugur Editorial · [1 sources] ·

Researchers from CUNY have developed a pipeline approach for analyzing mental health changes using social media data for the CLPsych 2026 Shared Task. Their system combines in-context learning from multiple open-weight large language models for classifying self-states and predicting timeline changes. The pipeline also includes a summarization component that leverages upstream predictions to describe mood dynamics over time, achieving top rankings in several task categories. AI

IMPACT Demonstrates a novel pipeline for analyzing mental health trends using LLMs, potentially improving early detection and intervention strategies.

RANK_REASON Academic paper detailing a novel approach to analyzing mental health changes using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CUNY team uses LLMs for mental health change analysis

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Academic paper detailing a novel approach to analyzing mental health changes using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
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123 days old
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Amirmohammad Ziaei Bideh, Shameed Charlomar Job, Ava Yahyapour, Alla Rozovskaya ·

    CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Changes

    arXiv:2605.24164v1 Announce Type: new Abstract: We describe our submission to the CLPsych~2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics. To infer the dominant self-states in posts (Tasks 1.1 and 1.2), we ensemble in-…

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