Comparing Similarity of HTML Structures and Affiliate IDs in Splog Analysis

  • Taichi Katayama
  • Akihito Morijiri
  • Soichi Ishii
  • Takehito Utsuro
  • Yasuhide Kawada
  • Tomohiro Fukuhara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6637)


Spam blogs or splogs are blogs hosting spam posts, created using machine generated or hijacked content for the sole purpose of hosting advertisements or raising the number of in-links of target sites. Among those splogs, this paper focuses on detecting a group of splogs which are estimated to be created by an identical spammer. In this paper, we compare two clues: namely, similarity of HTML structures of splogs and affiliate IDs automatically extracted from splogs. We first show that the similarity of HTML structures of splogs is quite effective in splog detection, as well as in identifying spammers. We then show that the identity of affiliate IDs extracted from splogs can identify spammers much more directly than similarity of HTML structures, although it is not easy to achieve high coverage in extracting affiliate IDs. Finally, we show that the coverage of the intersection of the two clues, similarity of HTML structures and affiliate IDs, is relatively low, and it is necessary to apply them in a complementary strategy.


spam blog detection HTML structures affiliate IDs 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Glance, N., Hurst, M., Tomokiyo, T.: Blogpulse: Automated trend discovery for Weblogs. In: Proc. Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics (2004)Google Scholar
  2. 2.
    Gyöngyi, Z., Garcia-Molina, H.: Web spam taxonomy. In: Proc. 1st AIRWeb, pp. 39–47 (2005)Google Scholar
  3. 3.
    Kolari, P., Joshi, A., Finin, T.: Characterizing the splogosphere. In: Proc. 3rd Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics (2006)Google Scholar
  4. 4.
    Macdonald, C., Ounis, I.: The TREC Blogs06 collection: Creating and analysing a blog test collection. Technical Report TR-2006-224, University of Glasgow, Department of Computing Science (2006)Google Scholar
  5. 5.
    Lin, Y.R., Sundaram, H., Chi, Y., Tatemura, J., Tseng, B.L.: Splog detection using self-similarity analysis on blog temporal dynamics. In: Proc. 3rd AIRWeb, pp. 1–8 (2007)Google Scholar
  6. 6.
    Wang, Y., Ma, M., Niu, Y., Chen, H.: Spam double-funnel: Connecting web spammers with advertisers. In: Proc. 16th WWW, pp. 291–300 (2007)Google Scholar
  7. 7.
    Sato, Y., Utsuro, T., Fukuhara, T., Kawada, Y., Murakami, Y., Nakagawa, H., Kando, N.: Analyzing features of Japanese splogs and characteristics of keywords. In: Proc. 4th AIRWeb, pp. 33–40 (2008)Google Scholar
  8. 8.
    Mishne, G., Carmel, D., Lempel, R.: Blocking blog spam with language model disagreement. In: Proc. 1st AIRWeb (2005)Google Scholar
  9. 9.
    Kolari, P., Finin, T., Joshi, A.: SVMs for the Blogosphere: Blog identification and Splog detection. In: Proc. 2006 AAAI Spring Symp. Computational Approaches to Analyzing Weblogs, pp. 92–99 (2006)Google Scholar
  10. 10.
    Katayama, T., Yoshinaka, T., Utsuro, T., Kawada, Y., Fukuhara, T.: Detecting splogs using similarities of splog HTML structures. In: Proc. 4th ICUIMC, pp. 256–263 (2010)Google Scholar
  11. 11.
    Urvoy, T., Lavergne, T., Filoche, P.: Tracking Web spam with hidden style similarity. In: Proc. 2nd AIRWeb, pp. 25–30 (2006)Google Scholar
  12. 12.
    Fukuhara, T., Kimura, A., Arai, Y., Yoshinaka, T., Masuda, H., Utsuro, T., Nakagawa, H.: KANSHIN: A cross-lingual concern analysis system using multilingual blog articles. In: Proc. 1st INGS 2008, pp. 83–90 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Taichi Katayama
    • 1
  • Akihito Morijiri
    • 1
  • Soichi Ishii
    • 2
  • Takehito Utsuro
    • 1
  • Yasuhide Kawada
    • 3
  • Tomohiro Fukuhara
    • 4
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Tokyo Denki UniversityTokyoJapan
  3. 3.Navix Co., Ltd.TokyoJapan
  4. 4.National Institute of Advanced Industrial Science and TechnologyTokyoJapan

Personalised recommendations