Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138244
Title: Unsupervised rumor detection based on users’ behaviors using neural networks
Authors: Chen, Weiling
Zhang, Yan
Yeo, Chai Kiat
Lau, Chiew Tong
Lee, Bu Sung
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Chen, W., Zhang, Y., Yeo, C. K., Lau, C. T., & Lee, B. S. (2018). Unsupervised rumor detection based on users’ behaviors using neural networks. Pattern Recognition Letters, 105, 226-233. doi:10.1016/j.patrec.2017.10.014
Journal: Pattern Recognition Letters
Abstract: Online social networks have become the hotbeds of many rumors as information can propagate much faster than ever. In order to detect the few but potentially harmful rumors to prevent the public issues they may cause, we propose an unsupervised learning model combining Recurrent Neural Networks and Autoencoders to distinguish rumors as anomalies from other credible microblogs based on users’ behaviors. Some features based on comments posted by other users are newly proposed and are then analyzed over their posting time so as to exploit the crowd wisdom to improve the detection performance. The experimental results show that our model achieves a high accuracy of 92.49% and F1 measure of 89.16%.
URI: https://hdl.handle.net/10356/138244
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2017.10.014
Rights: © 2017 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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