CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Changes
For participants in the CLPsych shared task, this work provides a competitive pipeline approach, though it is an incremental application of existing methods.
The authors developed a pipeline for classifying and summarizing mental health changes from social media timelines, using ensemble in-context learning and supervised classifiers. Their system ranked first on Task 1.1, fourth on Tasks 1.2 and 2, and third on Task 3.1.
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