@inproceedings{chopra-etal-2024-deciphering,
title = "Deciphering psycho-social effects of Eating Disorder : Analysis of {R}eddit Posts using Large Language Model({LLM})s and Topic Modeling",
author = "Chopra, Medini and
Chatterjee, Anindita and
Dey, Lipika and
Das, Partha Pratim",
editor = {H{\"a}m{\"a}l{\"a}inen, Mika and
{\"O}hman, Emily and
Miyagawa, So and
Alnajjar, Khalid and
Bizzoni, Yuri},
booktitle = "Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities",
month = nov,
year = "2024",
address = "Miami, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nlp4dh-1.15",
pages = "156--164",
abstract = "Eating disorders are a global health concern as they manifest in increasing numbers across all sections of society. Social network platforms have emerged as a dependable source of information about the disease, its effect, and its prevalence among different sections. This work lays the foundation for large-scale analysis of social media data using large language models (LLMs). We show that using LLMs can drastically reduce the time and resource requirements for garnering insights from large data repositories. With respect to ED, this work focuses on understanding its psychological impacts on both patients and those who live in their proximity. Social scientists can utilize the proposed approach to design more focused studies with better representative groups.",
}
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<abstract>Eating disorders are a global health concern as they manifest in increasing numbers across all sections of society. Social network platforms have emerged as a dependable source of information about the disease, its effect, and its prevalence among different sections. This work lays the foundation for large-scale analysis of social media data using large language models (LLMs). We show that using LLMs can drastically reduce the time and resource requirements for garnering insights from large data repositories. With respect to ED, this work focuses on understanding its psychological impacts on both patients and those who live in their proximity. Social scientists can utilize the proposed approach to design more focused studies with better representative groups.</abstract>
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%0 Conference Proceedings
%T Deciphering psycho-social effects of Eating Disorder : Analysis of Reddit Posts using Large Language Model(LLM)s and Topic Modeling
%A Chopra, Medini
%A Chatterjee, Anindita
%A Dey, Lipika
%A Das, Partha Pratim
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Miyagawa, So
%Y Alnajjar, Khalid
%Y Bizzoni, Yuri
%S Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, USA
%F chopra-etal-2024-deciphering
%X Eating disorders are a global health concern as they manifest in increasing numbers across all sections of society. Social network platforms have emerged as a dependable source of information about the disease, its effect, and its prevalence among different sections. This work lays the foundation for large-scale analysis of social media data using large language models (LLMs). We show that using LLMs can drastically reduce the time and resource requirements for garnering insights from large data repositories. With respect to ED, this work focuses on understanding its psychological impacts on both patients and those who live in their proximity. Social scientists can utilize the proposed approach to design more focused studies with better representative groups.
%U https://aclanthology.org/2024.nlp4dh-1.15
%P 156-164
Markdown (Informal)
[Deciphering psycho-social effects of Eating Disorder : Analysis of Reddit Posts using Large Language Model(LLM)s and Topic Modeling](https://aclanthology.org/2024.nlp4dh-1.15) (Chopra et al., NLP4DH 2024)
ACL