The addition of ‘trust’ and ‘distrust’ buttons on social media, alongside standard ‘like’ buttons, could help to reduce the spread of misinformation, finds a new experimental study led by UCL researchers.
Incentivising accuracy cut in half the reach of false posts, according to the findings published in eLife.
Co-lead author, Professor Tali Sharot (UCL Psychology & Language Sciences, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, and Massachusetts Institute of Technology) said: “Over the past few years, the spread of misinformation, or ‘fake news’, has skyrocketed, contributing to the polarisation of the political sphere and affecting people’s beliefs on anything from vaccine safety to climate change to tolerance of diversity. Existing ways to combat this, such as flagging inaccurate posts, have had limited impact.
“Part of why misinformation spreads so readily is that users are rewarded with ‘likes’ and ‘shares’ for popular posts, but without much incentive to share only what’s true.
“Here, we have designed a simple way to incentivise trustworthiness, which we found led to a large reduction in the amount of misinformation being shared.”
In another recent paper, published in Cognition, Professor Sharot and colleagues found that people were more likely to share statements on social media that they had previously been exposed to, as people saw repeated information as more likely to be accurate, demonstrating the power of repetition of misinformation.*
For the latest study, they sought to test out a potential solution, using a simulated social media platform used by 951 study participants across six experiments. The platforms involved users sharing news articles, half of which were inaccurate, and other users could react not only with ‘like’ or ‘dislike’ reactions, and repost stories, but in some versions of the experiment users could also react with ‘trust’ or ‘distrust’ reactions.
The researchers found that the incentive structure was popular, as people used the trust/distrust buttons more than like/dislike buttons, and it was also effective, as users started posting more true than false information in order to gain ‘trust’ reactions. Further analysis using computational modelling revealed that after the introduction of trust/distrust reactions, participants were also paying more attention to how reliable a news story appeared to be when deciding whether to repost it.
Additionally, the researchers found that after using the platform, those who had been using the versions with trust/distrust buttons ended up with more accurate beliefs.