N达勒姆.H. — It can be challenging to gauge the quality of online news — questioning if it is real or if it is fake. When it comes to health news and press releases about medical treatments and procedures the issue can be even more complex, especially if the story is not complete and still doesn’t necessarily fall into the category of fake news. To help identify the stories with inflated claims, inaccuracies and possible associated risks, researchers at the 永利app新版本官网地址 developed a new machine learning model, an application of artificial intelligence, 新闻服务, 比如社交媒体, could easily use to better screen medical news stories for accuracy. 

“的 way most people think about fake news is something that's completely fabricated, 但, 尤其是在医疗保健领域, 它不需要是假的. It could be that maybe they're not mentioning something,埃米拉·兹弗拉说, assistant professors of decision sciences at UNH’s 彼得T. 保罗商学院 and 经济学. “In the study, we’re not making claims about the intent of the news organizations that put these out. But if things are left out, there should be a way to look at that.”  

Zifla and study co-author Burcu Eke Rubini, assistant professors of decision sciences, 在他们的研究中, 发表在 决策支持系统, that since most people don’t have the medical expertise to understand the complexities of the news, the machine learning models they developed outperformed the evaluations of laypeople in assessing the quality of health stories. 的y used data from Health News Review that included news stories and press releases on new healthcare treatments 发表在 various outlets from 2013 to 2018. 的 articles had already been evaluated by a panel of healthcare experts—medical doctors, healthcare journalists and clinical professors—using ten different evaluation criteria the experts had developed. 的 criteria included cost and benefits of the treatment or test, 任何可能的伤害, 争论的质量, the novelty and availability of the procedure and the independence of the sources. 的 researchers then developed an algorithm based on the same expert criteria, and trained the machine models to classify each aspect of the  news story, matching that criteria as "satisfactory" or "not satisfactory". 

的 model's performance was then compared against layperson evaluations obtained through a separate survey where participants rated the same articles as "satisfactory" or "not satisfactory" based on the same criteria. 的 survey revealed an "optimism bias," with most of the 254 participants rating articles as satisfactory, markedly different from the model's more critical assessments. 

Researchers stress that they are by no means looking to replace expert opinion 但 are hoping to start a conversation about evaluating news based on multiple criteria and offering an easily accessible and low-cost alternative via open-source software to better evaluate health news.

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