基于多层自编码器的在线社交网络谣言检测

Yan Zhang, Weiling Chen, C. Yeo, C. Lau, Bu-Sung Lee
{"title":"基于多层自编码器的在线社交网络谣言检测","authors":"Yan Zhang, Weiling Chen, C. Yeo, C. Lau, Bu-Sung Lee","doi":"10.1109/TEMSCON.2017.7998415","DOIUrl":null,"url":null,"abstract":"Rumors spread on Online Social Networks sometimes can lead to serious social issues. To accurately identify them from normal posts is proved to be of great value. Users' behaviors are different when they post rumors and normal posts. Since rumors only account for a small percentage of all posts, they can be regarded as anomalies. Therefore, we propose an anomaly detection method based on autoencoder to perform rumor detection. In this paper we propose several self-adapting thresholds which can facilitate rumor detection. In addition, we further discuss how the different number of hidden layers of autoencoder can influence the detection performance. The experimental results show that our model achieves a good F1 and a low false positive rate.","PeriodicalId":193013,"journal":{"name":"2017 IEEE Technology & Engineering Management Conference (TEMSCON)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Detecting rumors on Online Social Networks using multi-layer autoencoder\",\"authors\":\"Yan Zhang, Weiling Chen, C. Yeo, C. Lau, Bu-Sung Lee\",\"doi\":\"10.1109/TEMSCON.2017.7998415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rumors spread on Online Social Networks sometimes can lead to serious social issues. To accurately identify them from normal posts is proved to be of great value. Users' behaviors are different when they post rumors and normal posts. Since rumors only account for a small percentage of all posts, they can be regarded as anomalies. Therefore, we propose an anomaly detection method based on autoencoder to perform rumor detection. In this paper we propose several self-adapting thresholds which can facilitate rumor detection. In addition, we further discuss how the different number of hidden layers of autoencoder can influence the detection performance. The experimental results show that our model achieves a good F1 and a low false positive rate.\",\"PeriodicalId\":193013,\"journal\":{\"name\":\"2017 IEEE Technology & Engineering Management Conference (TEMSCON)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Technology & Engineering Management Conference (TEMSCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TEMSCON.2017.7998415\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Technology & Engineering Management Conference (TEMSCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TEMSCON.2017.7998415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30

摘要

在社交网络上传播的谣言有时会导致严重的社会问题。在正常岗位上准确识别它们具有重要的价值。用户发布谣言和正常发帖的行为是不同的。由于谣言只占所有帖子的一小部分,因此可以视为异常。因此,我们提出了一种基于自编码器的异常检测方法来进行谣言检测。在本文中,我们提出了几个自适应阈值,以方便谣言检测。此外,我们还进一步讨论了不同隐藏层数对自动编码器检测性能的影响。实验结果表明,该模型具有较好的F1和较低的误报率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting rumors on Online Social Networks using multi-layer autoencoder
Rumors spread on Online Social Networks sometimes can lead to serious social issues. To accurately identify them from normal posts is proved to be of great value. Users' behaviors are different when they post rumors and normal posts. Since rumors only account for a small percentage of all posts, they can be regarded as anomalies. Therefore, we propose an anomaly detection method based on autoencoder to perform rumor detection. In this paper we propose several self-adapting thresholds which can facilitate rumor detection. In addition, we further discuss how the different number of hidden layers of autoencoder can influence the detection performance. The experimental results show that our model achieves a good F1 and a low false positive rate.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信