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    <abstract>The recognition of mental health’s crucial significance has led to a growing interest in utilizing social media text data in current research trends. However, there remains a significant gap in the study of panic and anxiety on these platforms, despite their high prevalence and severe impact. In this paper, we address this gap by presenting a dataset consisting of 1,930 user posts from Quora and Reddit specifically focusing on panic and anxiety. Through a combination of lexical analysis, emotion detection, and writer attitude assessment, we explore the unique characteristics of each condition. To gain deeper insights, we employ a mental health-specific transformer model and a large language model for qualitative analysis. Our findings not only contribute to the understanding digital discourse on anxiety and panic but also provide valuable resources for the broader research community. We make our dataset, methodologies, and code available to advance understanding and facilitate future studies.</abstract>
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%0 Conference Proceedings
%T Comparing panic and anxiety on a dataset collected from social media
%A Mitrović, Sandra
%A Lithgow-Serrano, Oscar William
%A Schillaci, Carlo
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F mitrovic-etal-2024-comparing
%X The recognition of mental health’s crucial significance has led to a growing interest in utilizing social media text data in current research trends. However, there remains a significant gap in the study of panic and anxiety on these platforms, despite their high prevalence and severe impact. In this paper, we address this gap by presenting a dataset consisting of 1,930 user posts from Quora and Reddit specifically focusing on panic and anxiety. Through a combination of lexical analysis, emotion detection, and writer attitude assessment, we explore the unique characteristics of each condition. To gain deeper insights, we employ a mental health-specific transformer model and a large language model for qualitative analysis. Our findings not only contribute to the understanding digital discourse on anxiety and panic but also provide valuable resources for the broader research community. We make our dataset, methodologies, and code available to advance understanding and facilitate future studies.
%U https://aclanthology.org/2024.clpsych-1.12
%P 153-165

Markdown (Informal)

[Comparing panic and anxiety on a dataset collected from social media](https://aclanthology.org/2024.clpsych-1.12) (Mitrović et al., CLPsych-WS 2024)

• Comparing panic and anxiety on a dataset collected from social media (Mitrović et al., CLPsych-WS 2024)

ACL

• Sandra Mitrović, Oscar William Lithgow-Serrano, and Carlo Schillaci. 2024. Comparing panic and anxiety on a dataset collected from social media. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 153–165, St. Julians, Malta. Association for Computational Linguistics.