@inproceedings{naskar-conway-2026-hierarchical,
title = "Hierarchical Multi-Stage Modeling of Adaptive and Maladaptive Self-States in Social Media Timelines",
author = "Naskar, Abir and
Conway, Mike",
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.37/",
pages = "472--481",
ISBN = "979-8-89176-421-7",
abstract = "We address the CLPsych 2026 Shared Task on modeling psychological self-states from longitudinal social media data. We propose (i) a hierarchical multi-stage framework that integrates a multi-task transformer encoder and (ii) a four stage instruction-tuned large language model finetuning pipeline for subelement classification, presence estimation, and evidence extraction. Our approach incorporates element-conditioned label masking and cross-stage encoder transfer, enabling structured prediction aligned with the ABCD psychological framework. Experiments show improvements over the baseline on the development setup, with RoBERTa achieving an 8.3{\textbackslash}{\%} gain in macro-F1 and improved RMSE, while a fine-tuned Qwen3 model attains the best overall performance. These results demonstrate the effectiveness of combining hierarchical multi-task learning with structured generation for interpretable mental health analysis."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="naskar-conway-2026-hierarchical">
<titleInfo>
<title>Hierarchical Multi-Stage Modeling of Adaptive and Maladaptive Self-States in Social Media Timelines</title>
</titleInfo>
<name type="personal">
<namePart type="given">Abir</namePart>
<namePart type="family">Naskar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mike</namePart>
<namePart type="family">Conway</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aya</namePart>
<namePart type="family">Zirikly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kfir</namePart>
<namePart type="family">Bar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sean</namePart>
<namePart type="family">MacAvaney</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Molly</namePart>
<namePart type="family">Ireland</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yaakov</namePart>
<namePart type="family">Ophir</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dana</namePart>
<namePart type="family">Atzil-Slonim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vasudha</namePart>
<namePart type="family">Varadarajan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bedrick</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bart</namePart>
<namePart type="family">Desmet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-421-7</identifier>
</relatedItem>
<abstract>We address the CLPsych 2026 Shared Task on modeling psychological self-states from longitudinal social media data. We propose (i) a hierarchical multi-stage framework that integrates a multi-task transformer encoder and (ii) a four stage instruction-tuned large language model finetuning pipeline for subelement classification, presence estimation, and evidence extraction. Our approach incorporates element-conditioned label masking and cross-stage encoder transfer, enabling structured prediction aligned with the ABCD psychological framework. Experiments show improvements over the baseline on the development setup, with RoBERTa achieving an 8.3\textbackslash% gain in macro-F1 and improved RMSE, while a fine-tuned Qwen3 model attains the best overall performance. These results demonstrate the effectiveness of combining hierarchical multi-task learning with structured generation for interpretable mental health analysis.</abstract>
<identifier type="citekey">naskar-conway-2026-hierarchical</identifier>
<location>
<url>https://aclanthology.org/2026.clpsych-1.37/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>472</start>
<end>481</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Hierarchical Multi-Stage Modeling of Adaptive and Maladaptive Self-States in Social Media Timelines
%A Naskar, Abir
%A Conway, Mike
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F naskar-conway-2026-hierarchical
%X We address the CLPsych 2026 Shared Task on modeling psychological self-states from longitudinal social media data. We propose (i) a hierarchical multi-stage framework that integrates a multi-task transformer encoder and (ii) a four stage instruction-tuned large language model finetuning pipeline for subelement classification, presence estimation, and evidence extraction. Our approach incorporates element-conditioned label masking and cross-stage encoder transfer, enabling structured prediction aligned with the ABCD psychological framework. Experiments show improvements over the baseline on the development setup, with RoBERTa achieving an 8.3\textbackslash% gain in macro-F1 and improved RMSE, while a fine-tuned Qwen3 model attains the best overall performance. These results demonstrate the effectiveness of combining hierarchical multi-task learning with structured generation for interpretable mental health analysis.
%U https://aclanthology.org/2026.clpsych-1.37/
%P 472-481
Markdown (Informal)
[Hierarchical Multi-Stage Modeling of Adaptive and Maladaptive Self-States in Social Media Timelines](https://aclanthology.org/2026.clpsych-1.37/) (Naskar & Conway, CLPsych 2026)
ACL