Khandelwal, VedantGaur, ManasKursuncu, UgurShalin, ValerieSheth, Amit2024-12-112024-12-112024-11-11https://doi.org/10.48550/arXiv.2411.07163http://hdl.handle.net/11603/37106Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, enhancing the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (approximately 12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92\%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance.13 pagesen-USAttribution 4.0 International CC BY 4.0http://creativecommons.org/licenses/by/4.0/UMBC Ebiquity Research GroupComputer Science - Artificial IntelligenceText