Abstract

The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages2633-2639
Number of pages7
StatePublished - 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period8/3/138/9/13

Fingerprint

Spamming
Information dissemination
Availability
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Hu, X., Tang, J., Zhang, Y., & Liu, H. (2013). Social spammer detection in microblogging. In IJCAI International Joint Conference on Artificial Intelligence (pp. 2633-2639)

Social spammer detection in microblogging. / Hu, Xia; Tang, Jiliang; Zhang, Yanchao; Liu, Huan.

IJCAI International Joint Conference on Artificial Intelligence. 2013. p. 2633-2639.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hu, X, Tang, J, Zhang, Y & Liu, H 2013, Social spammer detection in microblogging. in IJCAI International Joint Conference on Artificial Intelligence. pp. 2633-2639, 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, 8/3/13.
Hu X, Tang J, Zhang Y, Liu H. Social spammer detection in microblogging. In IJCAI International Joint Conference on Artificial Intelligence. 2013. p. 2633-2639
Hu, Xia ; Tang, Jiliang ; Zhang, Yanchao ; Liu, Huan. / Social spammer detection in microblogging. IJCAI International Joint Conference on Artificial Intelligence. 2013. pp. 2633-2639
@inproceedings{87d5de728b3e4e7580918850f855e392,
title = "Social spammer detection in microblogging",
abstract = "The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of {"}human{"} friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.",
author = "Xia Hu and Jiliang Tang and Yanchao Zhang and Huan Liu",
year = "2013",
language = "English (US)",
isbn = "9781577356332",
pages = "2633--2639",
booktitle = "IJCAI International Joint Conference on Artificial Intelligence",

}

TY - GEN

T1 - Social spammer detection in microblogging

AU - Hu, Xia

AU - Tang, Jiliang

AU - Zhang, Yanchao

AU - Liu, Huan

PY - 2013

Y1 - 2013

N2 - The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.

AB - The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.

UR - http://www.scopus.com/inward/record.url?scp=84896063139&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84896063139&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84896063139

SN - 9781577356332

SP - 2633

EP - 2639

BT - IJCAI International Joint Conference on Artificial Intelligence

ER -