Basically, as we are flooded with more and more posts, our ability to discriminate between real and fake becomes blurred.
At relatively low flows of information, his algorithm predicted that a theoretical social media user was able to discriminate between genuine and fake news well, sharing mostly genuine news. However, as Oliveira and his coauthors tweaked the algorithm to reflect greater and greater flows of information—the equivalent of scrolling through an endless Twitter or Facebook feed—the theoretical user proved less and less capable of sorting quality information from bad information.
Oliveira found that, in general, popularity had a stronger effect on whether a person shared something than quality. At higher levels of information flow that effect became more pronounced, meaning people would theoretically spend less or no time assessing the information’s quality before deciding to share it. Soon, as they paid less and less attention to each piece of information, the people were sharing fake news at higher and higher rates.
At the highest rates modeled, the quality of a piece of information had zero effect on the popularity of that information. “We show that both information overload and limited attention contribute to a degradation in the system’s discriminative power,” Oliveira said via email.