@inproceedings{fernandez-iglesias-etal-2024-depressmind,
title = "{D}epress{M}ind: A Depression Surveillance System for Social Media Analysis",
author = "Fern{\'a}ndez-Iglesias, Roque and
Fernandez-Pichel, Marcos and
Aragon, Mario and
Losada, David E.",
editor = "Aletras, Nikolaos and
De Clercq, Orphee",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-demo.5",
pages = "35--43",
abstract = "Depression is a pressing global issue that impacts millions of individuals worldwide. This prevailing psychologicaldisorder profoundly influences the thoughts and behavior of those who suffer from it. We have developed DepressMind, a versatile screening tool designed to facilitate the analysis of social network data. This automated tool explores multiple psychological dimensions associated with clinical depression and estimates the extent to which these symptoms manifest in language use. Our project comprises two distinct components: one for data extraction and another one for analysis.The data extraction phase is dedicated to harvesting texts and the associated meta-information from social networks and transforming them into a user-friendly format that serves various analytical purposes.For the analysis, the main objective is to conduct an in-depth inspection of the user publications and establish connections between the posted contents and dimensions or traits defined by well-established clinical instruments.Specifically, we aim to associate extracts authored by individuals with symptoms or dimensions of the Beck Depression Inventory (BDI).",
}
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<abstract>Depression is a pressing global issue that impacts millions of individuals worldwide. This prevailing psychologicaldisorder profoundly influences the thoughts and behavior of those who suffer from it. We have developed DepressMind, a versatile screening tool designed to facilitate the analysis of social network data. This automated tool explores multiple psychological dimensions associated with clinical depression and estimates the extent to which these symptoms manifest in language use. Our project comprises two distinct components: one for data extraction and another one for analysis.The data extraction phase is dedicated to harvesting texts and the associated meta-information from social networks and transforming them into a user-friendly format that serves various analytical purposes.For the analysis, the main objective is to conduct an in-depth inspection of the user publications and establish connections between the posted contents and dimensions or traits defined by well-established clinical instruments.Specifically, we aim to associate extracts authored by individuals with symptoms or dimensions of the Beck Depression Inventory (BDI).</abstract>
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%0 Conference Proceedings
%T DepressMind: A Depression Surveillance System for Social Media Analysis
%A Fernández-Iglesias, Roque
%A Fernandez-Pichel, Marcos
%A Aragon, Mario
%A Losada, David E.
%Y Aletras, Nikolaos
%Y De Clercq, Orphee
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F fernandez-iglesias-etal-2024-depressmind
%X Depression is a pressing global issue that impacts millions of individuals worldwide. This prevailing psychologicaldisorder profoundly influences the thoughts and behavior of those who suffer from it. We have developed DepressMind, a versatile screening tool designed to facilitate the analysis of social network data. This automated tool explores multiple psychological dimensions associated with clinical depression and estimates the extent to which these symptoms manifest in language use. Our project comprises two distinct components: one for data extraction and another one for analysis.The data extraction phase is dedicated to harvesting texts and the associated meta-information from social networks and transforming them into a user-friendly format that serves various analytical purposes.For the analysis, the main objective is to conduct an in-depth inspection of the user publications and establish connections between the posted contents and dimensions or traits defined by well-established clinical instruments.Specifically, we aim to associate extracts authored by individuals with symptoms or dimensions of the Beck Depression Inventory (BDI).
%U https://aclanthology.org/2024.eacl-demo.5
%P 35-43
Markdown (Informal)
[DepressMind: A Depression Surveillance System for Social Media Analysis](https://aclanthology.org/2024.eacl-demo.5) (Fernández-Iglesias et al., EACL 2024)
ACL
- Roque Fernández-Iglesias, Marcos Fernandez-Pichel, Mario Aragon, and David E. Losada. 2024. DepressMind: A Depression Surveillance System for Social Media Analysis. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 35–43, St. Julians, Malta. Association for Computational Linguistics.