@inproceedings{chakraborty-etal-2025-self,
    title = "Self-State Evidence Extraction and Well-Being Prediction from Social Media Timelines",
    author = "Chakraborty, Suchandra  and
      Jana, Sudeshna  and
      Sinha, Manjira  and
      Dasgupta, Tirthankar",
    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://preview.aclanthology.org/fix-sig-urls/2025.clpsych-1.24/",
    pages = "279--286",
    ISBN = "979-8-89176-226-8",
    abstract = "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)."
}