@inproceedings{sandu-etal-2025-capturing,
title = "Capturing the Dynamics of Mental Well-Being: Adaptive and Maladaptive States in Social Media",
author = "Sandu, Anastasia and
Mihailescu, Teodor and
Uban, Ana Sabina and
Bucur, Ana-Maria",
editor = "Zirikly, Ayah and
Yates, Andrew and
Desmet, Bart and
Ireland, Molly and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ophir, Yaakov",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clpsych-1.18/",
doi = "10.18653/v1/2025.clpsych-1.18",
pages = "225--234",
ISBN = "979-8-89176-226-8",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Capturing the Dynamics of Mental Well-Being: Adaptive and Maladaptive States in Social Media
%A Sandu, Anastasia
%A Mihailescu, Teodor
%A Uban, Ana Sabina
%A Bucur, Ana-Maria
%Y Zirikly, Ayah
%Y Yates, Andrew
%Y Desmet, Bart
%Y Ireland, Molly
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ophir, Yaakov
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-226-8
%F sandu-etal-2025-capturing
%X 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.
%R 10.18653/v1/2025.clpsych-1.18
%U https://aclanthology.org/2025.clpsych-1.18/
%U https://doi.org/10.18653/v1/2025.clpsych-1.18
%P 225-234
Markdown (Informal)
[Capturing the Dynamics of Mental Well-Being: Adaptive and Maladaptive States in Social Media](https://aclanthology.org/2025.clpsych-1.18/) (Sandu et al., CLPsych 2025)
ACL