United States

We provide an overview of the CLPsych 2025 Shared Task, which focuses on capturing mental health dynamics from social media timelines. Building on CLPsych 2022’s longitudinal modeling approach, this work combines monitoring mental states with evidence and summary generation through four subtasks: (A.1) Evidence Extraction, highlighting text spans reflecting adaptive or maladaptive self-states; (A.2) Well-Being Score Prediction, assigning posts a 1 to 10 score based on social, occupational, and psychological functioning; (B) Post-level Summarization of the interplay between adaptive and maladaptive states within individual posts; and (C) Timeline-level Summarization capturing temporal dynamics of self-states over posts in a timeline. We describe key findings and future directions.

NAACL 2025

Overview of the CLPsych 2025 Shared Task: Capturing Mental Health Dynamics from Social Media Timelines

clpsych

span extraction

dynamics

shared task

mental health

summarization

social media

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

We present a baseline for the CLPsych 2025 A.1 task: classifying self-states in mental health data taken from Reddit. We use few-shot learning with a 4-bit quantized Gemma 2 9B model (Gemma Team, 2024; Brown et al., 2020; Daniel Han and team, 2023) and a data preprocessing step which first identifies relevant sentences indicating self-state evidence, and then performs a binary classification to determine whether the sentence is evidence of an adaptive or maladaptive self-state. This system outperforms our other method which relies on an LLM to highlight spans of variable length independently. We attribute the performance of our model to the benefits of this sentence chunking step for two reasons: partitioning posts into sentences 1) broadly matches the granularity at which self-states were human-annotated and 2) simplifies the task for our language model to a binary classification problem. Our system placed third out of fourteen systems submitted for Task A.1, earning a test-time recall of 0.579.

A baseline for self-state identification and classification in mental health data: CLPsych 2025 Task

This paper describes the contributions of the BLUE team in the CLPsych 2025 Shared Task on Capturing Mental Health Dynamics from Social Media Timelines. We participate in all tasks with three submissions, for which we use two sets of approaches: an unsupervised approach using prompting of various large language models (LLM) with no fine-tuning for this task or domain, and a supervised approach based on several lightweight machine learning models trained to classify sentences for evidence extraction, based on an augmented training dataset sourced from public psychological questionnaires. We obtain the best results for summarization Tasks B and C in terms of consistency, and the best F1 score in Task A.2.

Capturing the Dynamics of Mental Well-Being: Adaptive and Maladaptive States in Social Media

The increasing prevalence of mental health discourse on social media has created a need for automated tools to assess psychological wellbeing. In this study, we propose a structured framework for evidence extraction, well-being scoring, and summary generation, developed as part of the CLPsych 2025 shared task. Our approach integrates feature-based classification with context-aware language modeling to identify self-state indicators, predict well-being scores, and generate clinically relevant summaries. Our system achieved a recall of 0.56 for evidence extraction, an MSE of 3.89 in well-being scoring, and high consistency scores (0.612 post-level, 0.801 timeline-level) in summary generation, ensuring strong alignment with extracted evidence. With an overall good rank, our framework demonstrates robustness in social media-based mental health monitoring. By providing interpretable assessments of psychological states, our work contributes to early detection and intervention strategies, assisting researchers and mental health professionals in understanding online well-being trends and enhancing digital mental health support systems.

CIOL at CLPsych 2025: Using Large Lanuage Models for Understanding and Summarizing Clinical Texts

Large Language Models are increasingly used in the medical field, particularly in psychiatry where language plays a fundamental role in diagnosis. This study explores the use of open-source LLMs within the MIND framework. Specifically, we implemented a mixed-methods approach for the CLPsych 2025 shared task: (1) we used a combination of retrieval and few-shot learning approaches to highlight evidence of mental states within the text and to generate comprehensive summaries for post-level and timeline-level analysis, allowing for effective tracking of psychological state fluctuations over time (2) we developed different types of ensemble methods for well-being score prediction, combining Machine Learning and Optimization approaches on top of zero-shot LLMs predictions. Notably, for the latter task, our approach demonstrated the best performance within the competition.

From Evidence Mining to Meta-Prediction: a Gradient of Methodologies for Task-Specific Challenges in Psychological Assessment

Social media data is recognized for its usefulness in the early detection of mental disorders; however, there is a lack of research focused on modeling individuals’ longitudinal mental health dynamics. Moreover, fine-tuning large language models (LLMs) on large-scale, annotated datasets presents challenges due to privacy concerns and the difficulties on data collection and annotation. In this paper, we propose a novel approach for modeling mental health dynamics using hybrid LLMs, where we first apply both classification-based and generation-based models to identify adaptive and maladaptive evidence from individual posts. This evidence is then used to predict well-being scores and generate post-level and timeline-level summaries. Experimental results on the CLPsych 2025 shared task demonstrate the effectiveness of our method, with the generative-based model showing a marked advantage in evidence identification.

From Posts to Timelines: Modeling Mental Health Dynamics from Social Media Timelines with Hybrid LLMs

With the advent of modern Computational Linguistic techniques and the growing societal mental health crisis, we contribute to the field of Clinical Psychology by participating in the CLPsych 2025 shared task. This paper describes the methods and results obtained by the uOttawa team’s submission (which included a researcher from the National Institutes of Health in the USA, in addition to three researchers from the University of Ottawa, Canada). The task consists of four subtasks focused on modeling longitudinal changes in social media users’ mental states and generating accurate summaries of these dynamic self-states. Through prompt engineering of a modern large language model (Llama-3.3-70B-Instruct), the uOttawa team placed first, sixth, fifth, and second, respectively, for each subtask, amongst the other submissions. This work demonstrates the capacity of modern large language models to recognize nuances in the analysis of mental states and to generate summaries through carefully crafted prompting.

Prompt Engineering for Capturing Dynamic Mental Health Self States from Social Media Posts

This paper presents our approach to the CLPsych 2025 (Tseriotou et al., 2025) shared task, where our proposed system implements a comprehensive solution using In-Context Learning (ICL) with vector similarity to retrieve relevant examples that guide Large Language Models (LLMs) without specific fine-tuning. We leverage ICL to analyze self-states and mental health indicators across three tasks. We developed a pipeline architecture using Ollama, where we are running Llama 3.3 70B locally and specialized vector databases for post- and timeline-level examples. We experimented with different numbers of retrieved examples (k=5 and k=10) to optimize performance. Our results demonstrate the effectiveness of ICL for clinical assessment tasks, particularly when dealing with limited training data in sensitive domains. The system shows strong performance across all tasks, with particular strength in capturing self-state dynamics.

Retrieval-Enhanced Mental Health Assessment: Capturing Self-State Dynamics from Social Media Using In-Context Learning

This study explores the application of Large Language Models (LLMs) and supervised learning to analyze social media posts from Reddit users, addressing two key objectives: first, to extract adaptive and maladaptive self-state evidence that supports psychological assessment (Task A1); and second, to predict a well-being score that reflects the user’s mental state (Task A2). We propose i) a fine-tuned RoBERTa (Liu et al., 2019) model for Task A1 to identify self-state evidence spans and ii) evaluate two approaches for Task A2: a retrieval-augmented DeepSeek-7B (DeepSeek-AI et al., 2025) model and a Random Forest regression model trained on sentence embeddings. While LLM-based prompting utilizes contextual reasoning, our findings indicate that supervised learning provides more reliable numerical predictions. The RoBERTa model achieves the highest recall (0.602) for Task A1, and Random Forest regression outperforms DeepSeek-7B for Task A2 (MSE: 2.994 vs. 6.610). These results highlight the strengths and limitations of generative vs. supervised methods in mental health NLP, contributing to the development of privacy-conscious, resource-efficient approaches for psychological assessment. This work is part of the CLPsych 2025 shared task (Tseriotou et al., 2025).

Self-State Evidence Extraction and Well-Being Prediction from Social Media Timelines

We tackle the task by using a pretrained large language model (LLM) and in-context learning with template-based instructions to guide the LLM. To improve generation quality, we employ a two-step procedure: sampling and selection. For the sampling step, we randomly sample a subset of the provided training data for the context of LLM prompting. Next, for the selection step, we map the LLM generated outputs into a vector space and employ the Gaussian kernel density estimation to select the most likely output. The results show that the approach can achieve a certain degree of performance and there is still room for improvement.

Team ISM at CLPsych 2025: Capturing Mental Health Dynamics from Social Media Timelines using A Pretrained Large Language Model with In-Context Learning

The CLPsych workshop, held annually since 2014, promotes the application of computational linguistics to behavioral analysis and neurological health assessment. The CLPsych 2025 shared task, extending the framework of the 2022 iteration, leverages the MIND framework to model temporal fluctuations in mental states. This shared task comprises three sub-tasks, each presenting substantial challenges to natural language processing (NLP) systems, requiring sensitive and precise outcomes in analyzing adaptive and maladaptive behaviors. In this study, we employed a range of modeling strategies tailored to the requirements and expected outputs of each subtask. Our approach mostly utilized traditional language models like BERT, LongFormer and Pegasus diverging from the prevalent trend of prompt-tuned large language models. We achieved an overall ranking of 13th, with subtask rankings of 8th in Task 1a, 13th in Task 1b, 8th in Task 2, and 7th in Task 3. These results highlight the efficacy of our methods while underscoring areas for further refinement in handling complex behavioral data.

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