@inproceedings{salama-etal-2026-temporal,
title = "Temporal Semantic Analysis of {A}rabic Mental-Health Discourse using Discrete Wavelet Transform",
author = "Salama, Rana and
Alqahtani, Amal and
Diab, Mona and
Youssef, Abdou",
editor = "Wang, Zimu and
Jiang, Yiwen and
Shen, Yaling and
Zhao, Xiangyu and
Fong, Stephanie and
Sun, Xin and
Zhou, Yi and
Ji, Shaoxiong and
Desimone, Robert and
Nelson, Barnaby and
Dwyer, Dominic and
Ge, Zongyuan",
booktitle = "Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy ({M}ulti{P}syche)",
month = nov,
year = "2026",
address = "Hengqin, Zhuhai, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.multipsyche-1.11/",
pages = "145--158",
ISBN = "979-8-89176-448-4",
abstract = "We present a multi-resolution framework for analyzing longitudinal Arabic mental-health discourse drawn from condition-specific Arabic-language X (formerly Twitter) communities. We model each user{'}s posting history as a calendar-time semantic trajectory and apply the Discrete Wavelet Transform (DWT) along the temporal axis to separate semantic variation occurring over finer and broader time scales. To interpret these temporal components linguistically, we introduce \textit{semantic gain}, which measures how strongly corpus-derived phrases become aligned with the reconstructed semantic trajectory when information from a given temporal scale is introduced. We evaluate the framework on longitudinal trajectories from BPD-related and bipolar-related X communities using independent temporal-change measures, chronological permutation tests, cross-scale phrase distinctiveness, and matched high- versus low- gain events. Finer-scale DWT components align most strongly with local adjacent semantic change, whereas broader-scale components show greater correspondence with distributional change measured by Maximum Mean Discrepancy (MMD). Our findings suggest that longitudinal mental-health discourse on X exhibits multiscale semantic structure, with different forms of linguistic change emerging over different temporal horizons. Combining wavelet decomposition with phrase-based semantic interpretation provides a principled way to characterize both the temporal scale and linguistic content of discourse change."
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<abstract>We present a multi-resolution framework for analyzing longitudinal Arabic mental-health discourse drawn from condition-specific Arabic-language X (formerly Twitter) communities. We model each user’s posting history as a calendar-time semantic trajectory and apply the Discrete Wavelet Transform (DWT) along the temporal axis to separate semantic variation occurring over finer and broader time scales. To interpret these temporal components linguistically, we introduce semantic gain, which measures how strongly corpus-derived phrases become aligned with the reconstructed semantic trajectory when information from a given temporal scale is introduced. We evaluate the framework on longitudinal trajectories from BPD-related and bipolar-related X communities using independent temporal-change measures, chronological permutation tests, cross-scale phrase distinctiveness, and matched high- versus low- gain events. Finer-scale DWT components align most strongly with local adjacent semantic change, whereas broader-scale components show greater correspondence with distributional change measured by Maximum Mean Discrepancy (MMD). Our findings suggest that longitudinal mental-health discourse on X exhibits multiscale semantic structure, with different forms of linguistic change emerging over different temporal horizons. Combining wavelet decomposition with phrase-based semantic interpretation provides a principled way to characterize both the temporal scale and linguistic content of discourse change.</abstract>
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%0 Conference Proceedings
%T Temporal Semantic Analysis of Arabic Mental-Health Discourse using Discrete Wavelet Transform
%A Salama, Rana
%A Alqahtani, Amal
%A Diab, Mona
%A Youssef, Abdou
%Y Wang, Zimu
%Y Jiang, Yiwen
%Y Shen, Yaling
%Y Zhao, Xiangyu
%Y Fong, Stephanie
%Y Sun, Xin
%Y Zhou, Yi
%Y Ji, Shaoxiong
%Y Desimone, Robert
%Y Nelson, Barnaby
%Y Dwyer, Dominic
%Y Ge, Zongyuan
%S Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy (MultiPsyche)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-448-4
%F salama-etal-2026-temporal
%X We present a multi-resolution framework for analyzing longitudinal Arabic mental-health discourse drawn from condition-specific Arabic-language X (formerly Twitter) communities. We model each user’s posting history as a calendar-time semantic trajectory and apply the Discrete Wavelet Transform (DWT) along the temporal axis to separate semantic variation occurring over finer and broader time scales. To interpret these temporal components linguistically, we introduce semantic gain, which measures how strongly corpus-derived phrases become aligned with the reconstructed semantic trajectory when information from a given temporal scale is introduced. We evaluate the framework on longitudinal trajectories from BPD-related and bipolar-related X communities using independent temporal-change measures, chronological permutation tests, cross-scale phrase distinctiveness, and matched high- versus low- gain events. Finer-scale DWT components align most strongly with local adjacent semantic change, whereas broader-scale components show greater correspondence with distributional change measured by Maximum Mean Discrepancy (MMD). Our findings suggest that longitudinal mental-health discourse on X exhibits multiscale semantic structure, with different forms of linguistic change emerging over different temporal horizons. Combining wavelet decomposition with phrase-based semantic interpretation provides a principled way to characterize both the temporal scale and linguistic content of discourse change.
%U https://aclanthology.org/2026.multipsyche-1.11/
%P 145-158
Markdown (Informal)
[Temporal Semantic Analysis of Arabic Mental-Health Discourse using Discrete Wavelet Transform](https://aclanthology.org/2026.multipsyche-1.11/) (Salama et al., MultiPsyche 2026)
ACL