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The quick rise of digital communication, especially on social media sites, has had a big effect on study in areas like crisis tracking, finding false information, and analyzing how people feel about things. This study looks at the progress made in Natural Language Processing (NLP), especially the addition of Large Language Models (LLMs) for quick reaction to crises and finding false information. We test how well different deep learning designs work. These include transformer-based models and mixed methods that use rule-based rules. It shows that a mixed model that combines LLMs with heuristic methods greatly enhances classification accuracy, precision, and memory by looking at crisis-related datasets such as CrisisMMD. Our results show that the suggested mix model is more accurate than pure deep learning methods at detecting misinformation and figuring out how people feel about things (94.5%). Even though mixed models make things easier to understand and lower the cost of computing, problems like reducing bias and being able to change in real time are still areas that need more study.

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Enhancing Crisis Response and Misinformation Detection Using Large Language Models and Hybrid Approaches

  • 会议论文

  • 发表日期：

    2026年

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  • Sri Rekha Uppuluri,
  • Sasanko Sekhar Gantayat

摘要

The quick rise of digital communication, especially on social media sites, has had a big effect on study in areas like crisis tracking, finding false information, and analyzing how people feel about things. This study looks at the progress made in Natural Language Processing (NLP), especially the addition of Large Language Models (LLMs) for quick reaction to crises and finding false information. We test how well different deep learning designs work. These include transformer-based models and mixed methods that use rule-based rules. It shows that a mixed model that combines LLMs with heuristic methods greatly enhances classification accuracy, precision, and memory by looking at crisis-related datasets such as CrisisMMD. Our results show that the suggested mix model is more accurate than pure deep learning methods at detecting misinformation and figuring out how people feel about things (94.5%). Even though mixed models make things easier to understand and lower the cost of computing, problems like reducing bias and being able to change in real time are still areas that need more study.