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-context learning of three open-weight large language models using majority voting. For predicting moments of change in a timeline (Task~2), we train supervised classifiers on features derived from Task~1. 1 predictions.

To summarize the patterns of mood dynamics and their progression over time within a timeline (Task 3. 1), we augment in-context example labels predicted by upstream systems (Tasks 1. 1, 1. 2, and 2), yielding performance gains over zero-shot and unaugmented in-context learning baselines. Our submission ranked first on Task~1. 1, fourth on Task~1. 2, fourth on Task~2, and third on Task~3. 1. \footnote{The source code for the experiments is available at https://github. com/amirzia/clpsych26-cuny