Information overload to blame for low quality of viral content
Social network modelling finds that fake news, clickbait and other low quality content only takes off online when readers are already swamped, writes Ariella Heffernan-Marks.
A team led by Xiaoyan Qiu from the Shanghai Institute of Technology used statistical models to show that the spread of low quality information over social media networks is linked to limited reader attention and high information load. This potentially explains why fake news, hoax stories and unsupported claims manage to go viral on social media.
An agent-based model was used to conduct the research. An ‘agent’ represented a person, and a ‘meme’ represented a new idea, image, post or message. Probability analysis was used to determine the quality of the meme, the rate of new memes being introduced, the popularity of the meme, the information diversity of the network, the attention of the agent and the discriminative power of the agent.
Multiple correlative studies were performed between these variables. The major finding of the study indicated that discriminative power decreased with an increase in the introduction of new memes, and with a decrease in agent attention.
In other words, content overload and an inability to pay significant attention to social media posts leads to an inability to discriminate between good and bad information.
The authors suggest that one way to improve the quality of online content would be by “limiting the number of posts in the system”, which would give people a better chance of telling the good stuff from the nonsense. One way to do this would be to curb the software-controlled bot accounts that currently flood social media with high volumes of low-quality material.
Another option would be for readers to pay a little bit more attention and attempt to be more selective with what they share or re-post. Something to keep in mind next time you’re scrolling through your news feeds, perhaps.