Unsupervised rumor detection based on users’ behaviors using neural networks

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 A...

Full description

Saved in:
Bibliographic Details
Main Authors: Chen, Weiling, Zhang, Yan, Yeo, Chai Kiat, Lau, Chiew Tong, Lee, Bu Sung
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/138244
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-138244
record_format dspace
spelling sg-ntu-dr.10356-1382442020-04-29T08:29:05Z Unsupervised rumor detection based on users’ behaviors using neural networks Chen, Weiling Zhang, Yan Yeo, Chai Kiat Lau, Chiew Tong Lee, Bu Sung School of Computer Science and Engineering Engineering::Computer science and engineering Online Social Networks Rumor Detection 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%. 2020-04-29T08:29:05Z 2020-04-29T08:29:05Z 2017 Journal Article 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 0167-8655 https://hdl.handle.net/10356/138244 10.1016/j.patrec.2017.10.014 2-s2.0-85031810640 105 226 233 en Pattern Recognition Letters © 2017 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Online Social Networks
Rumor Detection
spellingShingle Engineering::Computer science and engineering
Online Social Networks
Rumor Detection
Chen, Weiling
Zhang, Yan
Yeo, Chai Kiat
Lau, Chiew Tong
Lee, Bu Sung
Unsupervised rumor detection based on users’ behaviors using neural networks
description 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%.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Chen, Weiling
Zhang, Yan
Yeo, Chai Kiat
Lau, Chiew Tong
Lee, Bu Sung
format Article
author Chen, Weiling
Zhang, Yan
Yeo, Chai Kiat
Lau, Chiew Tong
Lee, Bu Sung
author_sort Chen, Weiling
title Unsupervised rumor detection based on users’ behaviors using neural networks
title_short Unsupervised rumor detection based on users’ behaviors using neural networks
title_full Unsupervised rumor detection based on users’ behaviors using neural networks
title_fullStr Unsupervised rumor detection based on users’ behaviors using neural networks
title_full_unstemmed Unsupervised rumor detection based on users’ behaviors using neural networks
title_sort unsupervised rumor detection based on users’ behaviors using neural networks
publishDate 2020
url https://hdl.handle.net/10356/138244
_version_ 1681056698457915392