Online Health Information

January 13, 2024

The rise of social media platforms as Online Health Information Sources (OHIS) has increased the spread of health misinformation in cyberspace. The rapid dissemination of false or misleading health information, particularly in public health, can have severe consequences. Misinformation not only endangers public health but also poses significant cybersecurity risks, including eroding trust in credible sources, enabling phishing attacks, and heightening the impact of cyber threats during crises.
Misinformation can escalate cyber threats, facilitate phishing and social engineering attacks, and compromise national security by misleading the public and causing confusion. It can delay incident responses, distort public perception during cyber incidents, and jeopardize data privacy by deceiving individuals into sharing sensitive information. Real-time detection through automated systems is crucial for mitigating these risks. This research aims to address misinformation using advanced techniques like Natural Language Processing (NLP), Knowledge Graphs, and Semantic Reasoners, with a particular focus on strengthening cybersecurity.
We have built a comprehensive framework to detect, quantify and combat misinformation using AI techniques like NLP, Knowledge Graphs, and Semantic Reasoners.

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