# News Labeling as Early as Possible: Real or Fake?

## TL;DR

This paper presents a novel recurrent neural network model with a unique loss function and stopping rule for early and accurate fake news detection in social networks, balancing timeliness and correctness.

## Contribution

It introduces a new early detection model that explicitly incorporates earliness into both the training and prediction processes, utilizing user profiles and diffusion speed.

## Key findings

- Effective early labeling demonstrated on real datasets.
- Outperforms existing baseline models in accuracy and timeliness.
- Balances early detection with high classification accuracy.

## Abstract

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.