The proliferation of health misinformation online poses a significant threat to public well-being and erodes trust in scientific consensus. Artificial Intelligence and Natural Language Processing offer powerful tools for identifying and countering such misinformation across digital platforms. By examining techniques like concept clustering and bot detection as applied to e-cigarette discussions on social media, this paper illuminates how these technologies can detect problematic content and proactively promote accurate scientific information. The analysis reveals patterns in how misinformation spreads through automated accounts, emotional triggers, and network effects. Beyond detection capabilities, AI can generate accessible scientific content, tailor communication to address public concerns, and personalize health messaging for diverse audiences. Despite promising applications, implementation faces challenges including distinguishing nuance from falsehood, addressing algorithmic bias, balancing free expression with harm prevention, ensuring system transparency, adapting to evolving tactics, and integrating human oversight effectively. Developing ethical AI solutions for health communication requires balancing technological capabilities with human expertise while safeguarding fundamental rights.
Keywords: Artificial Intelligence, bot detection, health misinformation, information ecosystems, sentiment analysis
