Uncovering cross-platform spreading patterns of fake news about COVID-19

Abstract

The spreading of fake news or misinformation on social media is a serious threat to modern societies, making more and more people susceptible to being unfairly influenced in their decision-making, be it in elections or other democratic processes. We contribute to the body of work in the area of fake news detection by studying cross-platform, multivariate spreading patterns of fake news on Covid-19-related topics – where existing studies have focused strongly on single platforms and/or on single metrics or indicators. Our findings show that there are several attributes that are specific to the cross-platform spreading process that become important predictors of fake news: there is e.g. a clear tendency that fake news travels faster from one platform to the other than real news. Meanwhile, although we have compiled a cross-platform corpus of fake and real news that future research may build on, data availability remains a challenge for future work.