A New Preprocessing Method for Rumor Detection in Social Networks based on LSTM-CNN

Recently, using social networks increases and people propagate their information through this networks. One of the most important challenges in these networks is sentiment attack, in which the attacker spreads rumors to influence users. Therefor rumor detection become important and attracts expanding research attention. Most of the previous works using deep neural networks for rumor detection without special preprocessing but we propose a new method for preprocessing data before learning which improve results. we use LSTM-CNN architecture with cyclical learning rate to detect Persian rumors. Beside that we investigate BERT model for Persian tweets. Our results demonstrate the effectiveness of this approach for English and Persian rumor detection.