@inproceedings{dasgupta-etal-2026-graph,
    title = "A Graph-Augmented Liquid Neural Network for Extracting Food Hazards and Disease Outbreaks",
    author = "Dasgupta, Tirthankar  and
      Sinha, Manjira  and
      Jana, Sudeshna  and
      Saha, Diya  and
      Verma, Ishan  and
      Aggarwal, Vaishali",
    editor = {Danilova, Vera  and
      Kurfal{\i}, Murathan  and
      S{\"o}derfeldt, Ylva  and
      Reed, Julia  and
      Burchell, Andrew},
    booktitle = "Proceedings of the 1st Workshop on Linguistic Analysis for Health ({H}ea{L}ing 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.healing-1.6/",
    pages = "67--76",
    ISBN = "979-8-89176-367-8",
    abstract = "The increasing frequency of foodborne illnesses, safety hazards, and disease outbreaks in the food supply chain demands urgent attention to protect public health. These incidents, ranging from contamination to intentional adulteration of food and feed, pose serious risks to consumers, leading to poisoning, and disease outbreaks that lead to product recalls. Identifying and tracking the sources and pathways of contamination is essential for timely intervention and prevention. This paper explores the use of social media and regulatory news reports to detect food safety issues and disease outbreaks. We present an automated approach leveraging a multi-task sequence labeling and sequence classification model that uses a liquid time-constant neural network augmented with a graph convolution network to extract and analyze relevant information from social media posts and official reports. Our methodology includes the creation of annotated datasets of social media content and regulatory documents, enabling the model to identify foodborne infections and safety hazards in real-time. Preliminary results demonstrate that our model outperforms baseline models, including advanced large language models like LLAMA-3 and Mistral-7B, in terms of accuracy and efficiency. The integration of liquid neural networks significantly reduces computational and memory requirements, achieving superior performance with just $1.2 \times e^6$ bytes of memory, compared to the 20.3 GB of GPU memory needed by traditional transformer-based models. This approach offers a promising solution for leveraging social media data in monitoring and mitigating food safety risks and public health threats."
}