Asymmetric self-learning for tackling Twitter spam drift

Chen, Chao, Zhang, Jun, Xiang, Yang and Zhou, Wanlei 2015, Asymmetric self-learning for tackling Twitter spam drift, in INFOCOM WKSHPS 2015: Proceedings of the Computer Communications Workshops, IEEE, Piscataway, N.J., pp. 208-213, doi: 10.1109/INFCOMW.2015.7179386.

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Title Asymmetric self-learning for tackling Twitter spam drift
Author(s) Chen, Chao
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Conference name IEEE Conference on Computer Communications Workshops (2015 : Hong Kong)
Conference location Hong Kong
Conference dates 26 Apr. - 1 May 2015
Title of proceedings INFOCOM WKSHPS 2015: Proceedings of the Computer Communications Workshops
Publication date 2015
Start page 208
End page 213
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Spam has become a critical problem on Twitter. In order to stop spammers, security companies apply blacklisting services to filter spam links. However, over 90% victims will visit a new malicious link before it is blocked by blacklists. To eliminate the limitation of blacklists, researchers have proposed a number of statistical features based mechanisms, and applied machine learning techniques to detect Twitter spam. In our labelled large dataset, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing ML based classifiers are poor. This phenomenon is referred as 'Twitter Spam Drift'. In order to tackle this problem, we carry out deep analysis of 1 million spam tweets and 1 million non-spam tweets, and propose an asymmetric self-learning (ASL) approach. The proposed ASL can discover new information of changed tweeter spam and incorporate it into classifier training process. A number of experiments are performed to evaluate the ASL approach. The results show that the ASL approach can be used to significantly improve the spam detection accuracy of using traditional ML algorithms.
ISBN 9781467371315
ISSN 0743-166X
Language eng
DOI 10.1109/INFCOMW.2015.7179386
Field of Research 080303 Computer System Security
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082893

Document type: Conference Paper
Collections: School of Information Technology
2018 ERA Submission
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