  Making disguise between real and fake news propagation through online social
networks is an important issue in many applications. The time gap between the
news release time and detection of its label is a significant step towards
broadcasting the real information and avoiding the fake. Therefore, one of the
challenging tasks in this area is to identify fake and real news in early
stages of propagation. However, there is a trade-off between minimizing the
time gap and maximizing accuracy. Despite recent efforts in detection of fake
news, there has been no significant work that explicitly incorporates early
detection in its model. In this paper, we focus on accurate early labeling of
news, and propose a model by considering earliness both in modeling and
prediction. The proposed method utilizes recurrent neural networks with a novel
loss function, and a new stopping rule. Given the context of news, we first
embed it with a class-specific text representation. Then, we utilize the
available public profile of users, and speed of news diffusion, for early
labeling of the news. Experiments on real datasets demonstrate the
effectiveness of our model both in terms of early labelling and accuracy,
compared to the state of the art baseline and models.
