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In recent years, especially following the COVID-19 outbreak, the spread of misleading public health information on social networking sites has gained new significance and scale. Misinformation campaigns now pose a serious threat to the credibility of information, eroding public trust in authorities and healthcare institutions, and underscoring the urgent need for effective detection. This paper introduces a system specifically designed to detect public health misinformation campaigns. Leveraging Large Language Models such as Llama 3.1 8B and Natural Language Processing (NLP), our system analyzes linguistic patterns to accurately interpret tweet context and distinguish misinformation from factual content. This approach empowers individuals, organizations, and policymakers to make well-informed decisions, fostering social cohesion and strengthening public trust. Our system is powered by a large, curated dataset compiled from 17 representative fake news datasets collected from three trusted sources. These datasets include both real and fake news, covering general topics as well as specific public health issues, particularly COVID-19, and are gathered from social media platforms like Twitter and various news outlets. Leveraging the Llama 3.1 8B Instruct LLM, our model achieves a remarkable 99% accuracy in detecting public health misinformation.

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Leveraging Large Language Models for Misinformation Detection: A Focus on Public Health Misinformation on Social Media

  • 会议论文

  • 发表日期：

    2026年

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  • Alanoud Alarabid,
  • Maryam Ali,
  • Eman Ba Abbad,
  • Ahmed Al Bloushi,
  • Heba Ismail

摘要

In recent years, especially following the COVID-19 outbreak, the spread of misleading public health information on social networking sites has gained new significance and scale. Misinformation campaigns now pose a serious threat to the credibility of information, eroding public trust in authorities and healthcare institutions, and underscoring the urgent need for effective detection. This paper introduces a system specifically designed to detect public health misinformation campaigns. Leveraging Large Language Models such as Llama 3.1 8B and Natural Language Processing (NLP), our system analyzes linguistic patterns to accurately interpret tweet context and distinguish misinformation from factual content. This approach empowers individuals, organizations, and policymakers to make well-informed decisions, fostering social cohesion and strengthening public trust. Our system is powered by a large, curated dataset compiled from 17 representative fake news datasets collected from three trusted sources. These datasets include both real and fake news, covering general topics as well as specific public health issues, particularly COVID-19, and are gathered from social media platforms like Twitter and various news outlets. Leveraging the Llama 3.1 8B Instruct LLM, our model achieves a remarkable 99% accuracy in detecting public health misinformation.