Detecting fake news by RNN-based gatekeeping behavior model on social networks

Social network users are not only news disseminators and consumers, but also gatekeepers. News gatekeepers are regular users who actively participate in news propagation. This study introduces the concept of gatekeepers into social network fake news detection and then presents a recurrent neural network (RNN)based gatekeeping behavior model (RGBM). Based on this, we propose a social network fake news detection method. The proposed method includes model training and fake-news detection. In the fake news detection phase, every observation sequence is updated in real time and the output of every observation sequence is calculated in real time. Therefore, the proposed method can detect social network fake news in real time. Several RNNs are compared on real datasets from Twitter and Weibo. The experimental results show that the gate recurrent unit (GRU) achieves the best comprehensive performance. On the Twitter and Weibo datasets, the proposed method had an overall accuracy of 0.985, recall of 0.978, F1 of 0.976 and loss of 0.058. In a comparison test, the proposed method outperformed several state-of-the-art approaches. The experimental results of the timeliness evaluation also demonstrated that the proposed method can effectively detect fake news in the early and middle stages of news propagation.

Automated summary

Social network users are not only news disseminators and consumers, but also gatekeepers. This study introduces the concept of gatekeepers into social network fake news detection and proposes a recurrent neural network (RNN) based gatekeeping behavior model (RGBM). The proposed method can detect social network fake news in real time and achieved high accuracy, recall, and F1 score in experiments on Twitter and Weibo datasets. It outperformed several state-of-the-art approaches and showed effectiveness in detecting fake news in early and middle stages of news propagation.