Study shows that the models outperform lay evaluations

Ermira Zifla and Burcu Eke Rubini, assistant professors of decision sciences at the UNH Peter T. Paul College of Business and Economics, recently trained machine learning models to evaluate the quality of health news stories about new medical treatments.  

Their work, published in Decision Support Systems, found that the machine learning models outperformed laypeople evaluations in assessing the quality of these health stories.     

The research tackles the complex challenge of determining the reliability of news that can be more nuanced – instances where the whole story isn’t being told but doesn’t fall into the category of fake news.

This challenge can be more pronounced with the quick and wide dissemination of news stories and press releases about new medical treatments because such stories can feature inflated claims and suppression of associated risks. At the same time, most ordinary people don’t have the medical expertise to understand some of these complexities.

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Ermira Zifla and Burcu Eke Rubini

Ermira Zifla and Burcu Eke Rubini