Fake news becomes a palpable potential risk to society because of the growing use of mobile devices and the immense increase in Internet access across the world . It is  essential to develop a simple mathematical model to understand the mechanism of the online dissemination of fake news. In this paper , we propose a point process model for predicting the spreading of the fake news on Twitter. This model describes the cascade as a two-stage process: initially, a cascade spreads as an ordinary newsstory; a second cascade thenemerges through attempts to disclose and rectify the falsity of the news  story. We validate this model through the collection of two datasets of fake news cascades from Twitter. We show that the proposed model is superior to the current state-of-the-art methods in accurately predicting the evolution of the fake news cascades . Moreover, the proposed model can appropriately infer the correction time when some users realize the falsity of the news  . The proposed model contributes to understand the dynamics of fake news spread in social mediaand is potentially advantageous in extracting a compact representation of the temporal information of the cascades .
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 .
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