@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://preview.aclanthology.org/jlcl-multiple-ingestion/2024.nlp4dh-1.15/",
    doi = "10.18653/v1/2024.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."
}