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Pandemic Misinformation Spreads Fast. Can Machine Learning Help?

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As information on the COVID-19 pandemic spreads at a rate rivaling that of the virus itself, separating trustworthy information from sensational and false news is paramount.

As information on the COVID-19 pandemic spreads at a rate rivaling that of the virus itself, separating trustworthy information from sensational and false news is paramount. While the pandemic illuminates the dangers of misleading health stories, researchers have been investigating methods to detect misinformation long before the outbreak. They hope these algorithmic tools will help social media users find the best information and avoid articles that are intentionally misleading.

In a recent study published in IEEE Access, Yue Liu et al. proposed a new machine learning algorithm that distinguishes reliable from unreliable health-related information. Liu et al.’s study focused on China because of its aging population and the prevalent use of e-health to acquire medical knowledge. They reviewed 4,381 health-related articles and classified them as either “reliable” or “unreliable.” Articles deemed reliable were written by doctors, medical researchers, or published by government-sponsored organizations. Conversely, articles with health-related rumors propagated through Chinese social media websites like WeChat or Weibo were classified as unreliable. Through this categorization method, the researchers obtained 2,296 pieces of reliable health information and 2,085 pieces of unreliable information.

The researchers began building their algorithm by generating summary statistics of keywords and writing styles of each type of story. Prior analysis suggested unreliable articles generally are designed to go viral. They tend to use hot-button issues like cancer, weight loss, and vaccines. Conversely, reliable articles covered a wider range of topics, aiming to highlight disease facts, present medical findings, or dispel rumors. The authors also differentiated reliable articles from unreliable articles though title length. Unreliable articles tended to use exaggerated language and click-bait terms, resulting in longer titles. In contrast, reliable articles used...