News Will Find Me Mindset Boosts Algorithm Trust

Pennsylvania State University

One in three people believe they don't have to seek the news from traditional outlets like newspapers and television. Instead, they think the "news will find me" (NFM), relying on algorithms and social networks to get their information. A research team led by Penn State scholars recently found that these individuals often consider their online networks to be as trustworthy as professional editors and journalists.

This mindset may make people more vulnerable to believing and sharing misinformation, according to the researchers, who published their findings in the journal Social Media & Society.

To understand news consumption behavior, the researchers designed an experiment that allowed them to observe how individuals with different levels of NFM engage with news. The researchers found users with higher NFM considered news recommended by algorithms or shared by others in their social network to be just as credible as news recommended by editors and reporters.

However, mid- and low-NFM individuals more critically evaluated news sources and placed higher value on stories from editors and reporters.

"The good news is that, overall, professionals are still valued," said corresponding author S. Shyam Sundar, Evan Pugh University Professor and James P. Jimirro Professor of Media Effects at Penn State. "But people with this tendency to rely on news coming to them - which is becoming more and more people - are trusting algorithms and social media friends to be their news sources."

When readers grant algorithms and social networks the same authority as journalists, it's easy for bad actors to manipulate that digital space versus imitating a trusted news source, the researchers said.

"The underlying psychological mechanism was not parsed out in previous studies," said first author Mengqi Liao, assistant professor at the University of Georgia who completed her doctoral studies with Sundar at Penn State. "We did this experiment to understand and explain why respondents evaluate the recommended news the way they do."

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