@inproceedings{phu-van-2026-nlp,
    title = "An {NLP} Framework for Analyzing Corporate Strategic Behavior in the Opioid Industry Documents Archive",
    author = "Phu, Duy Dang  and
      V{\u{a}}n, Th{\`i}n {\DJ}ặng",
    editor = "Card, Dallas  and
      Field, Anjalie  and
      Keith, Katherine  and
      Mendelsohn, Julia",
    booktitle = "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science",
    month = jul,
    year = "2026",
    address = "San Diego",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.nlpcss-1.7/",
    pages = "113--122",
    ISBN = "979-8-89176-426-2",
    abstract = "The Opioid Industry Documents Archive (OIDA) provides extensive internal corporate records that offer valuable insight into the drivers of the opioid crisis, yet its use in systematic analysis of corporate strategy remains limited. In this study, we propose an NLP-based framework to analyze strategic behavior in large-scale litigation archives, combining relevance filtering and topic modeling with large language model (LLM)-assisted interpretation. Applied to documents from Insys Therapeutics and Mallinckrodt Pharmaceuticals, our approach uncovers systematic differences in corporate strategies and organizational priorities. These results highlight the potential of integrating representation learning and LLMs for large-scale analysis in public health and corporate accountability research."
}