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A co-classification framework for detecting web spam and spammers in social media web sites

Authors:

Feilong Chen,

Pang-Ning Tan,

Anil K. JainAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1807 - 1810

https://doi.org/10.1145/1645953.1646235

Published: 02 November 2009 Publication History

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Abstract

Social media are becoming increasingly popular and have attracted considerable attention from spammers. Using a sample of more than ninety thousand known spam Web sites, we found between 7% to 18% of their URLs are posted on two popular social media Web sites, digg.com and delicious.com. In this paper, we present a co-classification framework to detect Web spam and the spammers who are responsible for posting them on the social media Web sites. The rationale for our approach is that since both detection tasks are related, it would be advantageous to train them simultaneously to make use of the labeled examples in the Web spam and spammer training data. We have evaluated the effectiveness of our algorithm on the delicious.com data set. Our experimental results showed that the proposed co-classification algorithm significantly outperforms classifiers that learn each detection task independently.

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Mvula PBranco PJourdan GViktor H(2024)A Survey on the Applications of Semi-supervised Learning to Cyber-securityACM Computing Surveys10.1145/365764756:10(1-41)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3657647
Chakraborty KBhattacharyya SBag R(2020)A Survey of Sentiment Analysis from Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29569577:2(450-464)Online publication date: Apr-2020
https://doi.org/10.1109/TCSS.2019.2956957
Li MWu BWang Y(2019)Comment Spam Detection via Effective Features CombinationICC 2019 - 2019 IEEE International Conference on Communications (ICC)10.1109/ICC.2019.8761340(1-6)Online publication date: May-2019
https://doi.org/10.1109/ICC.2019.8761340
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Index Terms

A co-classification framework for detecting web spam and spammers in social media web sites
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2009

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Mvula PBranco PJourdan GViktor H(2024)A Survey on the Applications of Semi-supervised Learning to Cyber-securityACM Computing Surveys10.1145/365764756:10(1-41)Online publication date: 22-Jun-2024
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Chakraborty KBhattacharyya SBag R(2020)A Survey of Sentiment Analysis from Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29569577:2(450-464)Online publication date: Apr-2020
https://doi.org/10.1109/TCSS.2019.2956957
Li MWu BWang Y(2019)Comment Spam Detection via Effective Features CombinationICC 2019 - 2019 IEEE International Conference on Communications (ICC)10.1109/ICC.2019.8761340(1-6)Online publication date: May-2019
https://doi.org/10.1109/ICC.2019.8761340
Wu FWu CLiu JCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Semi-Supervised Collaborative Learning for Social Spammer and Spam Message Detection in MicrobloggingProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3269324(1791-1794)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3269324
Mowlaei MAbadeh MKeshavarz H(2018)A Lexicon Generation Method for Aspect-Based Opinion Mining2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES)10.1109/INES.2018.8523897(000107-000112)Online publication date: Jun-2018
https://doi.org/10.1109/INES.2018.8523897
Wu FChuang YLai H(2018)Facilitating apps recommendation in Google PlayThe Electronic Library10.1108/EL-05-2017-011936:5(856-874)Online publication date: Oct-2018
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Zhang XLi ZZhu SLiang W(2016)Detecting Spam and Promoting Campaigns in TwitterACM Transactions on the Web10.1145/284610210:1(1-28)Online publication date: 8-Feb-2016
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Wu FShu JHuang YYuan Z(2016)Co-detecting social spammers and spam messages in microblogging via exploiting social contextsNeurocomputing10.1016/j.neucom.2016.03.036201:C(51-65)Online publication date: 12-Aug-2016
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Chakraborty MPal SPramanik RRavindranath Chowdary C(2016)Recent developments in social spam detection and combating techniquesInformation Processing and Management: an International Journal10.1016/j.ipm.2016.04.00952:6(1053-1073)Online publication date: 1-Nov-2016
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Zhang XBai HLiang W(2016)A Social Spam Detection Framework via Semi-supervised LearningRevised Selected Papers of the PAKDD 2016 Workshops on Trends and Applications in Knowledge Discovery and Data Mining - Volume 979410.1007/978-3-319-42996-0_18(214-226)Online publication date: 19-Apr-2016
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Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges

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Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges

Predicting web spam with HTTP session information

Effectively Detecting Content Spam on the Web Using Topical Diversity Measures

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