research-article

An empirical study on selective sampling in active learning for splog detection

Authors:

Taichi Katayama,

Takehito Utsuro,

Takayuki Yoshinaka,

Yasuhide Kawada,

Tomohiro FukuharaAuthors Info & Claims

AIRWeb '09: Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web

Pages 29 - 36

https://doi.org/10.1145/1531914.1531921

Published: 21 April 2009 Publication History

Abstract

This paper studies how to reduce the amount of human supervision for identifying splogs / authentic blogs in the context of continuously updating splog data sets year by year. Following the previous works on active learning, against the task of splog / authentic blog detection, this paper empirically examines several strategies for selective sampling in active learning by Support Vector Machines (SVMs). As a confidence measure of SVMs learning, we employ the distance from the separating hyperplane to each test instance, which have been well studied in active learning for text classification. Unlike those results of applying active learning to text classification tasks, in the task of splog / authentic blog detection of this paper, it is not the case that adding least confident samples peforms best.

References

[1]

Wikipedia, Spam blog. http://en.wikipedia.org/wiki/Spam_blog.

[2]

Wikipedia, Ping (blogging). http://en.wikipedia.org/wiki/Ping_(blogging).

[3]

N. Glance, M. Hurst, and T. Tomokiyo. Blogpulse: Automated trend discovery for Weblogs. In WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2004.

[4]

Z. Gyöngyi and H. Garcia-Molina. Web spam taxonomy. In Proc. 1st AIRWeb, pages 39--47, 2005.

[5]

P. Kolari, T. Finin, and A. Joshi. SVMs for the Blogosphere: Blog identification and Splog detection. In Proc. 2006 AAAI Spring Symp. Computational Approaches to Analyzing Weblogs, pages 92--99, 2006.

[6]

P. Kolari, T. Finin, and A. Joshi. Spam in blogs and social media. In Tutorial at ICWSM, 2007.

[7]

P. Kolari, A. Joshi, and T. Finin. Characterizing the splogosphere. In Proc. 3rd Ann. Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2006.

[8]

L. I. Kuncheva. Classifier ensembles for detecting concept change in streaming data: Overview and perspectives. In Proc. 2nd Workshop SUEMA 2008 (ECAI 2008), pages 5--10, 2008.

[9]

D. D. Lewis and W. A. Gale. A sequential algorithm for training text classifiers. In Proc. 17th SIGIR, pages 3--12, 1994.

Digital Library

[10]

Y.-R. Lin, H. Sundaram, Y. Chi, J. Tatemura, and B. L. Tseng. Splog detection using self-similarity analysis on blog temporal dynamics. In Proc. 3rd AIRWeb, pages 1--8, 2007.

Digital Library

[11]

C. Macdonald and I. Ounis. The TREC Blogs06 collection: Creating and analysing a blog test collection. Technical Report TR-2006-224, University of Glasgow, Department of Computing Science, 2006.

[12]

G. Mishne, D. Carmel, and R. Lempel. Blocking blog spam with language model disagreement. In Proc. 1st AIRWeb, 2005.

[13]

T. Nanno, T. Fujiki, Y. Suzuki, and M. Okumura. Automatically collecting, monitoring, and mining Japanese weblogs. In WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, pages 320--321. ACM Press, 2004.

Digital Library

[14]

Y. Sato, T. Utsuro, T. Fukuhara, Y. Kawada, Y. Murakami, H. Nakagawa, and N. Kando. Analysing features of Japanese splogs and characteristics of keywords. In Proc. 4th AIRWeb, 2008.

Digital Library

[15]

G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In Proc. 17th ICML, pages 839--846, 2000.

Digital Library

[16]

S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proc. 17th ICML, pages 999--1006, 2000.

Digital Library

[17]

V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, 1995.

Digital Library

[18]

Y. Wang, M. Ma, Y. Niu, and H. Chen. Spam double-funnel: Connecting web spammers with advertisers,. In Proc. 16th WWW Conf., pages 291--300, 2007.

Digital Library

Cited By

Goswami AKumar A(2017)Challenges in the Analysis of Online Social NetworksWireless Personal Communications: An International Journal10.1007/s11277-017-4712-397:3(4015-4061)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11277-017-4712-3
Ren YYang XYin M(2012)Detection Splog Algorithm Based on Features Relation TreeProceedings of the 2012 Ninth Web Information Systems and Applications Conference10.1109/WISA.2012.39(99-102)Online publication date: 16-Nov-2012
https://dl.acm.org/doi/10.1109/WISA.2012.39
Tan EGuo LChen SZhang XZhao Y(2012)Spammer Behavior Analysis and Detection in User Generated Content on Social NetworksProceedings of the 2012 IEEE 32nd International Conference on Distributed Computing Systems10.1109/ICDCS.2012.40(305-314)Online publication date: 18-Jun-2012
https://dl.acm.org/doi/10.1109/ICDCS.2012.40
Show More Cited By

Index Terms

An empirical study on selective sampling in active learning for splog detection
1. Information systems
  1. Information retrieval
  2. Information storage systems

Recommendations

SVM selective sampling for ranking with application to data retrieval
KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vector Machines) - a classification and regression methodology - have also ...
Support vector machine active learning with applications to text classification

Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, ...
Passive Sampling for Regression
ICDM '10: Proceedings of the 2010 IEEE International Conference on Data Mining

Active sampling (also called active learning or selective sampling) has been extensively researched for classification and rank learning methods, which is to select the most informative samples from unlabeled data such that, once the samples are labeled,...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

AIRWeb '09: Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web

April 2009

67 pages

ISBN:9781605584386

DOI:10.1145/1531914

Editors:
Dennis Fetterly
Microsoft Research
,
Zoltán Gyöngyi
Google Research

Copyright © 2009 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AIRWeb '09

AIRWeb '09: AIRWeb '09, 5th International Workshop on Adversarial Information Retrieval on the Web

April 21, 2009

Madrid, Spain

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
207
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Goswami AKumar A(2017)Challenges in the Analysis of Online Social NetworksWireless Personal Communications: An International Journal10.1007/s11277-017-4712-397:3(4015-4061)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11277-017-4712-3
Ren YYang XYin M(2012)Detection Splog Algorithm Based on Features Relation TreeProceedings of the 2012 Ninth Web Information Systems and Applications Conference10.1109/WISA.2012.39(99-102)Online publication date: 16-Nov-2012
https://dl.acm.org/doi/10.1109/WISA.2012.39
Tan EGuo LChen SZhang XZhao Y(2012)Spammer Behavior Analysis and Detection in User Generated Content on Social NetworksProceedings of the 2012 IEEE 32nd International Conference on Distributed Computing Systems10.1109/ICDCS.2012.40(305-314)Online publication date: 18-Jun-2012
https://dl.acm.org/doi/10.1109/ICDCS.2012.40
Ren YYin MWang J(2011)Dynamic Splog Filtering algorithm Based on Combinational FeaturesProceedings of the 2011 Eighth Web Information Systems and Applications Conference10.1109/WISA.2011.23(82-85)Online publication date: 21-Oct-2011
https://dl.acm.org/doi/10.1109/WISA.2011.23
Berendt B(2011)Spam, Opinions, and Other Relationships: Towards a Comprehensive View of the Web Knowledge DiscoveryAdvanced Topics in Information Retrieval10.1007/978-3-642-20946-8_3(51-82)Online publication date: 2011
https://doi.org/10.1007/978-3-642-20946-8_3
Katayama TYoshinaka TUtsuro TKawada YFukuhara TYou KLee SKim WWon D(2010)Detecting splogs using similarities of splog HTML structuresProceedings of the 4th International Conference on Uniquitous Information Management and Communication10.1145/2108616.2108661(1-8)Online publication date: 14-Jan-2010
https://dl.acm.org/doi/10.1145/2108616.2108661
Abu-Nimeh SChen T(2010)Proliferation and Detection of Blog SpamIEEE Security and Privacy10.1109/MSP.2010.1138:5(42-47)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1109/MSP.2010.113

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents