@inproceedings{chen-etal-2025-hassles,
title = "Hassles and Uplifts Detection on Social Media Narratives",
author = "Chen, Jiyu and
Karimi, Sarvnaz and
Molla, Diego and
Duenser, Andreas and
Kangas, Maria and
Paris, Cecile",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.28/",
pages = "475--489",
ISBN = "979-8-89176-298-5",
abstract = "Hassles and uplifts are psychological constructs of individuals' positive or negative responses to daily minor incidents, with cumulative impacts on mental health. These concepts are largely overlooked in NLP, where existing tasks and models focus on identifying general sentiment expressed in text. These, however, cannot satisfy targeted information needs in psychological inquiry. To address this, we introduce Hassles and Uplifts Detection (HUD), a novel NLP application to identify these constructs in social media language.We evaluate various language models and task adaptation approaches on a probing dataset collected from a private, real-time emotional venting platform. Some of our models achieve F scores close to 80{\%}. We also identify open opportunities to improve affective language understanding in support of studies in psychology."
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<abstract>Hassles and uplifts are psychological constructs of individuals’ positive or negative responses to daily minor incidents, with cumulative impacts on mental health. These concepts are largely overlooked in NLP, where existing tasks and models focus on identifying general sentiment expressed in text. These, however, cannot satisfy targeted information needs in psychological inquiry. To address this, we introduce Hassles and Uplifts Detection (HUD), a novel NLP application to identify these constructs in social media language.We evaluate various language models and task adaptation approaches on a probing dataset collected from a private, real-time emotional venting platform. Some of our models achieve F scores close to 80%. We also identify open opportunities to improve affective language understanding in support of studies in psychology.</abstract>
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%0 Conference Proceedings
%T Hassles and Uplifts Detection on Social Media Narratives
%A Chen, Jiyu
%A Karimi, Sarvnaz
%A Molla, Diego
%A Duenser, Andreas
%A Kangas, Maria
%A Paris, Cecile
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F chen-etal-2025-hassles
%X Hassles and uplifts are psychological constructs of individuals’ positive or negative responses to daily minor incidents, with cumulative impacts on mental health. These concepts are largely overlooked in NLP, where existing tasks and models focus on identifying general sentiment expressed in text. These, however, cannot satisfy targeted information needs in psychological inquiry. To address this, we introduce Hassles and Uplifts Detection (HUD), a novel NLP application to identify these constructs in social media language.We evaluate various language models and task adaptation approaches on a probing dataset collected from a private, real-time emotional venting platform. Some of our models achieve F scores close to 80%. We also identify open opportunities to improve affective language understanding in support of studies in psychology.
%U https://aclanthology.org/2025.ijcnlp-long.28/
%P 475-489
Markdown (Informal)
[Hassles and Uplifts Detection on Social Media Narratives](https://aclanthology.org/2025.ijcnlp-long.28/) (Chen et al., IJCNLP-AACL 2025)
ACL
- Jiyu Chen, Sarvnaz Karimi, Diego Molla, Andreas Duenser, Maria Kangas, and Cecile Paris. 2025. Hassles and Uplifts Detection on Social Media Narratives. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 475–489, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.