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:: Volume 4, Issue 1 (7-2020) ::
C4I Journal 2020, 4(1): 38-51 Back to browse issues page
A New Preprocessing Method for Rumor Detection in Social Networks based on LSTM-CNN
Maryam Khosravi , Hossein Shirazi * , Kourosh Dadahstabar , S.Alireza Hashemi Golpaygani
Malek-ashtar University of Technology
Abstract:   (3367 Views)
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.
Keywords: Deep learning, Preprocessing, Rumor detection, Social network
Full-Text [PDF 924 kb]   (2283 Downloads)    
Type of Study: Research | Subject: Artificial Intelligence
Received: 2020/03/12 | Accepted: 2020/07/14 | Published: 2020/11/20
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Khosravi M, Shirazi H, Dadahstabar K, Hashemi Golpaygani S. A New Preprocessing Method for Rumor Detection in Social Networks based on LSTM-CNN. C4I Journal 2020; 4 (1) :38-51
URL: http://ic4i-journal.ir/article-1-203-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 4, Issue 1 (7-2020) Back to browse issues page
فصلنامه علمی-پژوهشی فرماندهی و کنترل C4I Journal

 
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