Splog detection using content, time and link structures

Yu Ru Lin; Hari Sundaram; Yun Chi; Jun Tatemura; Belle Tseng

doi:10.1109/icme.2007.4285079

Splog detection using content, time and link structures

Yu Ru Lin, Hari Sundaram, Yun Chi, Jun Tatemura, Belle Tseng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper focuses on spam Hog (splog) detection. Blogs are highly popular, new media social communication mechanisms and splogs corrupt blog search results as well as waste network resources. In our approach we exploit unique blog temporal dynamics to detect splogs. The key idea is that splogs exhibit high temporal regularity in content and post time, as well as consistent linking patterns. Temporal content regularity is detected using a novel autocorrelation of post content. Temporal structural regularity is determined using the entropy of the post time difference distribution, while the link regularity is computed using a HITS based hub score measure. Experiments based on the annotated ground truth on real world dataset show excellent results on splog detection tasks with 90% accuracy.

Original language	English (US)
Title of host publication	Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
Publisher	IEEE Computer Society
Pages	2030-2033
Number of pages	4
ISBN (Print)	1424410177, 9781424410170
DOIs	https://doi.org/10.1109/icme.2007.4285079
State	Published - 2007
Externally published	Yes
Event	IEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China Duration: Jul 2 2007 → Jul 5 2007

Publication series

Name	Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Other

Other	IEEE International Conference onMultimedia and Expo, ICME 2007
Country/Territory	China
City	Beijing
Period	7/2/07 → 7/5/07

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Software

Online availability

10.1109/icme.2007.4285079

Library availability

Discover UIUC Full Text

Cite this

Lin, Y. R., Sundaram, H., Chi, Y., Tatemura, J., & Tseng, B. (2007). Splog detection using content, time and link structures. In Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007 (pp. 2030-2033). Article 4285079 (Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007). IEEE Computer Society. https://doi.org/10.1109/icme.2007.4285079

Splog detection using content, time and link structures. / Lin, Yu Ru; Sundaram, Hari; Chi, Yun et al.
Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007. IEEE Computer Society, 2007. p. 2030-2033 4285079 (Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, YR, Sundaram, H, Chi, Y, Tatemura, J & Tseng, B 2007, Splog detection using content, time and link structures. in Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007., 4285079, Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007, IEEE Computer Society, pp. 2030-2033, IEEE International Conference onMultimedia and Expo, ICME 2007, Beijing, China, 7/2/07. https://doi.org/10.1109/icme.2007.4285079

Lin YR, Sundaram H, Chi Y, Tatemura J, Tseng B. Splog detection using content, time and link structures. In Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007. IEEE Computer Society. 2007. p. 2030-2033. 4285079. (Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007). doi: 10.1109/icme.2007.4285079

@inproceedings{dc3b206ce12a46e3bff9f1e2c79ea101,

title = "Splog detection using content, time and link structures",

abstract = "This paper focuses on spam Hog (splog) detection. Blogs are highly popular, new media social communication mechanisms and splogs corrupt blog search results as well as waste network resources. In our approach we exploit unique blog temporal dynamics to detect splogs. The key idea is that splogs exhibit high temporal regularity in content and post time, as well as consistent linking patterns. Temporal content regularity is detected using a novel autocorrelation of post content. Temporal structural regularity is determined using the entropy of the post time difference distribution, while the link regularity is computed using a HITS based hub score measure. Experiments based on the annotated ground truth on real world dataset show excellent results on splog detection tasks with 90% accuracy.",

author = "Lin, {Yu Ru} and Hari Sundaram and Yun Chi and Jun Tatemura and Belle Tseng",

year = "2007",

doi = "10.1109/icme.2007.4285079",

language = "English (US)",

isbn = "1424410177",

series = "Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007",

publisher = "IEEE Computer Society",

pages = "2030--2033",

booktitle = "Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007",

note = "IEEE International Conference onMultimedia and Expo, ICME 2007 ; Conference date: 02-07-2007 Through 05-07-2007",

}

TY - GEN

T1 - Splog detection using content, time and link structures

AU - Lin, Yu Ru

AU - Sundaram, Hari

AU - Chi, Yun

AU - Tatemura, Jun

AU - Tseng, Belle

PY - 2007

Y1 - 2007

N2 - This paper focuses on spam Hog (splog) detection. Blogs are highly popular, new media social communication mechanisms and splogs corrupt blog search results as well as waste network resources. In our approach we exploit unique blog temporal dynamics to detect splogs. The key idea is that splogs exhibit high temporal regularity in content and post time, as well as consistent linking patterns. Temporal content regularity is detected using a novel autocorrelation of post content. Temporal structural regularity is determined using the entropy of the post time difference distribution, while the link regularity is computed using a HITS based hub score measure. Experiments based on the annotated ground truth on real world dataset show excellent results on splog detection tasks with 90% accuracy.

AB - This paper focuses on spam Hog (splog) detection. Blogs are highly popular, new media social communication mechanisms and splogs corrupt blog search results as well as waste network resources. In our approach we exploit unique blog temporal dynamics to detect splogs. The key idea is that splogs exhibit high temporal regularity in content and post time, as well as consistent linking patterns. Temporal content regularity is detected using a novel autocorrelation of post content. Temporal structural regularity is determined using the entropy of the post time difference distribution, while the link regularity is computed using a HITS based hub score measure. Experiments based on the annotated ground truth on real world dataset show excellent results on splog detection tasks with 90% accuracy.

UR - http://www.scopus.com/inward/record.url?scp=40949084945&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=40949084945&partnerID=8YFLogxK

U2 - 10.1109/icme.2007.4285079

DO - 10.1109/icme.2007.4285079

M3 - Conference contribution

AN - SCOPUS:40949084945

SN - 1424410177

SN - 9781424410170

T3 - Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

SP - 2030

EP - 2033

BT - Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

PB - IEEE Computer Society

T2 - IEEE International Conference onMultimedia and Expo, ICME 2007

Y2 - 2 July 2007 through 5 July 2007

ER -

Splog detection using content, time and link structures

Abstract

Publication series

Other

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this