  Fake news can have a significant negative impact on society because of the
growing use of mobile devices and the worldwide increase in Internet access. It
is therefore essential to develop a simple mathematical model to understand the
online dissemination of fake news. In this study, we propose a point process
model of the spread of fake news on Twitter. The proposed model describes the
spread of a fake news item as a two-stage process: initially, fake news spreads
as a piece of ordinary news; then, when most users start recognizing the
falsity of the news item, that itself spreads as another news story. We
validate this model using two datasets of fake news items spread on Twitter. We
show that the proposed model is superior to the current state-of-the-art
methods in accurately predicting the evolution of the spread of a fake news
item. Moreover, a text analysis suggests that our model appropriately infers
the correction time, i.e., the moment when Twitter users start realizing the
falsity of the news item. The proposed model contributes to understanding the
dynamics of the spread of fake news on social media. Its ability to extract a
compact representation of the spreading pattern could be useful in the
detection and mitigation of fake news.
