1. Spammer Detection based on Tagging Behavior in Social Bookmarking Systems [8][1][7] del.icio.us  ¹
Flickr ² [6] [13] [6] 1. 2. Public annotation services such as social book- marking services allow users to easily publicize con- tents with tags or tag existing content.  Therefore, they also provide (unintentionally) spammers with new markets.  We start with a dataset of tagging behavior that consists of instances labeled spam and non-spam and generate a classifier that detect the spammers from the features derived from user be- havior.  Evaluation results showed that incorporat- ing the proposed features into conventional schemes can reduce the misclassification of non-spammers users (false positives).  We examine social bookmark- ing data to show that thresholding the proposed fea- tures calculated generated from training data can classify spammers with very high precision. . . . a 1
2. Heymann [6] (detection) (prevention) 3 (demotion) Koutrika [11] ¹ http://delicious.com/ ² http://flickr.com/ 2009 Vol.7, No.4 3 ... ." 1 Table 1   RSDC '08 Dataset [12]
ECML PKDD 2008 Discovery Challenge (RSDC '08) 3 (Spam Detection in Social Bookmarking Systems) Borgers [2] language model Gkanogiannis [4] 3.1 RSDC '08 RSDC 708 Bib- 7 Sonomy  [9] (user spam, tas, tas spam, bookmark, bookmark spam, bib- tex, bibtex_spam) user_spam spam user_spam ID tas, 2008
3. [8] 1
³ http://www.kde.cs.uni-kassel.de/ws/rsdc08/ 2
20% tas spam bookmark URL bibtex bibtex Golder BibSonomy 5 2008 16 BibSonomy 2008
30
train-spam Chi [3] (social navigation efficiency) (social train-test tagging efficiency) 3.2 1. RSDC  08 4,036
2 users tags (unique) contents
65,508 45% 4,036 Kim
SVM Rocchio [5] Vol.7, No.4 2009 3 2 DBSJ Journal, Vol.7, No.4 March 2009 H ( T u | D u ) H(T u )
p(t j , d i ) log p(t j |d i )
T u
u Tu H ( T u ) (1) (2) (3) u D u 4.
H(D u |T u ) H(T u |D u ) 4.3.1んk = 15,120
ID 1しT . 4.3.2 5,6 21 1 H ( T u | D u ) y H ( T u | D u ) H(T α | D α ) H ( T u | D u ) 4.2 Chi [3] u u T u D u H(T u |D u ) 286 1,298 Vol.7, No.4 3 2009 2 Table 2   Common contents for spammers and non-spammers 3 H ( D u | T u ) spammer) tag # 2 spammer) Fig. 2 Cumulative tag number for each session ( k = 15, non-spammer) 100% 4 (k = 120, non- spammer) Fig. 4 Cumulative tag number for each session ( k = 120, non-spammer) 5. RSDC  '08 5.1 Baseline1 [12] 5.2 spamr cohamr cospamt, cohamt [12] cospamrt, cohamrt spamt spamr spamratio 7 spamratio(r/t/rt) cospam coham cospam(r/t/rt), coham(r/t/rt), Baseline2 2 F1 (false positive; FP) Vol.7, No.4 3 2009 4 3 Baseline1 Table 3   Baseline1
4   Baseline2 (LR) Table 4   Baseline2 (LR)
(LR) 5 Table 5   Proposed method (LR)
6 Table 6   Experimental results
6 Fi 3.2 28
5.3
Naive Bayes (NB)   Support Vector Decision Tree Machine (SVM)  Logistic Regression (LR) (J48) F 1 NB Weka [14] NaiveBayesMultinomial C4.5 J48 SVM LibSVM 5.4 LR
2 × 2 Baseline2 17 14 Baseline2 FP 8 6.
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