Presentation of PolaritySpam, a graph-based ranking algorithm intended to demote the spam web pages in the ranking provided by a web search engine. Cite as: F. Javier Ortega; Craig Macdonald; José A. Troyano; Fermín L. Cruz. “Spam Detection with a Content-based Random-Walk Algorithm”. Proceedings of the Second International Workshop on Search and Mining User-Generated Contents, International Conference on Information and Knowledge Management. 2010. Toronto, Canadá

1. Spam detection with
a content-based random-walk
algorithm
F. Javier Ortega Craig Macdonald
javierortega@us.es craigm@dcs.gla.ac.uk
José A. Troyano Fermín Cruz
troyano@us.es fcruz@us.es

2. Index
♦ Introduction
♦ Related work
♦ Content-based
♦ Link-based
♦ Our Approach
♦ Random-walk algorithm
♦ Content-based metrics
♦ Selection of seeds
♦ Experiments
♦ Future work
♦ References

3. Introduction
♦ Web Spam: phenomenon where a number
of web pages are created for the purpose
of making a search engine deliver
undesirable results for a given query.

4. Introduction
♦ Self-Promotion: gaining high relevance for a
search engine, mainly based on the textual
content.
i.e.: including a number of keywords in the web page.

5. Introduction
♦ Mutual-Promotion: gaining high score by
focusing the attention on the out-links and in-links
of a web page.
i.e.: a web page with lots of in-links
can be considered relevant by a search
engine.

6. Introduction
♦ Web Spam characteristics:
♦ Textual content: large amount of invisible
content, a set of words with high frequency,
lots of hyperlinks with large anchor texts, very
long words, etc.
♦ Link-farms: large number of pages pointing
one to another, in order to improve their scores
by increasing the number of in-links to them.
♦ Good pages usually point to good pages.
♦ Spam pages mainly point to other spam pages (link-
farms). They rarely point to good pages.

7. Related work: Content-based
♦ Content-based techniques classify the web pages as spam or
not-spam according to their textual content.
♦ Heuristics to determine the spam likehood of a web page.
♦ Meta tag content, anchor texts, URL of the page, average lenght of
the words, compression rate, etc. [10, 12]
♦ Inclusion of link-based scores and metrics into a classifier [3]
♦ Link-based techniques exploit the relations between web pages
to obtain a rank of pages, ordered according to their spam
likelihood.
♦ Random-Walk algorithms that penalizes spam-like behaviors.
♦ Don't take into account the nearest neighbours [1]
♦ Take only the scores received from a specific set of good or bad pages.
[7,11]

8. Our Approach
♦ Our approach combines both techniques:
♦ A set of content-based metrics, that
obtains information from each single web
page.
♦ A link-based algorithm, that processes the
relations between web pages.
♦ The goal is to obtain a ranking of web
pages, in which spam web pages are
demoted according to their spam
likelihood.

9. Our Approach
Web Content- Selection of
pages based metrics Seeds
Random-walk
algorithm
Web graph

10. Our Approach: random-walk algorithm
♦ We propose a random-walk algorithm that
computes two scores for each web page:
♦PR⁺: relevance of a web page
♦PR⁻: spam likelihood of a web page
♦ PR⁻(b), changes according to the relation of
b with spam-like web pages. Analogous with
PR⁺.
The higher PR⁺(a), the higher PR⁺(b).
a b
The higher PR⁻(a), the higher PR⁻(b).

11. Our Approach: random-walk algorithm
♦ Formula:
♦ Intuition:
High PR⁺ High PR⁻
Higher PR⁺!! Higher PR⁻!!

12. Our Approach: content-based metrics
♦ Content-based metrics are intended to
extract some a-priori information from the
textual content of the web pages.
♦ Content-based metrics must be:
♦ Easy to obtain: save the performance!
♦ Accurate: precision is preferred over recall.

13. Our Approach: content-based metrics
♦ Selected metrics:
♦ Compressibility: fraction of the sizes of a web
page, before and after being compressed.
♦ Fraction of globally popular words: a web
page with a high fraction of words within the
most popular words in the entire corpus, is
likely to be a spam.
♦ Average length of words: non-spam web
pages have a bell-shaped distribution of
average word lengths, while malicious pages
have much higher values.

14. Our Approach: selection of seeds
♦ Seeds: set of relevant nodes, in terms of
spam (negative seeds) or not-spam
likelihood (positive seeds).
♦ The algorithm gives more relevance to the
seeds.
♦ Spam-biased algorithm

15. Our Approach: selection of seeds
♦ Unsupervised method: content-based
metrics as features to choose the seeds.
♦ Pros:
♦Human intervention is not needed.
♦Larger number of seeds can be considered.
♦Inclusion of text content into a link-based
method.
♦ Due to the lack of human intervention...
♦“False positives”.

16. Our Approach: selection of seeds
♦ Obtaining a-priori score for a node, a:
♦ Selecting seeds:
♦ Pos/Neg Approach:
♦ Pos/Neg Metrics Approach:
♦ Metric-based Approach

17. Experiments
♦ Dataset: WEBSPAM-UK2006*
♦ ~98 million pages
♦ 11,402 hand-labeled hosts
♦ 7,423 labeled as spam.
♦ ~10 million spam web pages
♦ Terrier IR Platform
♦ Random-walk algorithm parameters:
♦ Damping factor = 0.85
♦ Threshold = 0.01
* C. Castillo, D. Donato, L. Becchetti, P. Boldi, S. Leonardi, M. Santini, and S. Vigna. A reference collection for
web spam. SIGIR Forum, 40(2):11–24, December 2006.

18. Experiments
♦ Evaluation: PR-buckets
Buckets Total Pages
1 14
PageRank
2 54
} PR-bucket 1 3 144
Relevance } PR-bucket 2 4 437
} PR-bucket 3
5
6
1070
2130
} PR-bucket 4
...
7
8
...
2664
2778
...
17 16M
18 28M
19 28M
20 28M
Total PR =

19. Experiments
♦ Baseline: TrustRank
♦ Link-based technique.
♦ Seeds chosen in a semi-supervised way:
• Hand-picked set of good pages.
• Top pages according to an inverse PageRank.
♦ Random-walk algorithm, biased according to the
seeds
Z. Gyongyi, H. Garcia-Molina, and J. Pedersen. Combating web
spam with trustrank. Technical Report 2004-17, Stanford
InfoLab, March 2004

20. Experiments
TrustRank Pos/Neg Approach
Pos/Neg Metrics Approach Metric-based Approach

21. Experiments
1000
100
10
1
1 2 3 4 5 6 7 8 9 10
TrustRank Pos/Neg Pos/Neg Metrics MetricsBased

22. Conclusions and future work
♦ Novel web spam detection technique, that combines
concepts from link and content-based methods.
♦ Content-based metrics as an unsupervised seed
selection method.
♦ Random-walk algorithm to compute two scores for each
web page: spam and not-spam likelihood.
♦ Future work:
♦ Including new content-based heuristics.
♦ Improving the spam-biased selection of the seeds,
taking into account the links to/from each node.
♦ Content-based metrics to characterize also the edges of
the web graph.

23. References
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Proceedings of the First International Workshop on Adversarial Information Retrieval on the Web (AIRWeb, 2005.
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[7] Z. Gyongyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. Technical Report 2004-17, Stanford
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24. Thanks for your attention!!
Questions?
F. Javier Ortega Craig Macdonald
javierortega@us.es craigm@dcs.gla.ac.uk
José A. Troyano Fermín Cruz
troyano@us.es fcruz@us.es