Fake News

A signal detection approach to understanding the identification of fake news

Research across many disciplines seeks to understand how misinformation spreads with a view towards limiting its impact. One important question in this research is how people determine whether a given piece of news is real or fake. The current article discusses the value of Signal Detection Theory (SDT) in disentangling two distinct aspects in the identification of fake news: (1) ability to accurately distinguish between real news and fake news and (2) response biases to judge news as real versus fake regardless of news veracity. The value of SDT for understanding the determinants of fake news beliefs is illustrated with reanalyses of existing data sets, providing more nuanced insights into how partisan bias, cognitive reflection, and prior exposure influence the identification of fake news. Implications of SDT for the use of source-related information in the identification of fake news, interventions to improve people’s skills in detecting fake news, and the debunking of misinformation are discussed.