Collective Spammer Detection in Evolving Multi-Relational Social Networks
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1 + Collective Spammer Detection in Evolving Multi-Relational Social Networks Shobeir Fakhraei (University of Maryland) James Foulds (University of California, Santa Cruz) Madhusudana Shashanka (if(we) Inc., Currently Niara Inc.) Lise Getoor (University of California, Santa Cruz)
2 Spam in Social Networks 2 n Recent study by Nexgate in 2013: n Spam grew by more than 300% in half a year
3 Spam in Social Networks 3 n Recent study by Nexgate in 2013: n Spam grew by more than 300% in half a year n 1 in 200 social messages are spam
4 Spam in Social Networks 4 n Recent study by Nexgate in 2013: n Spam grew by more than 300% in half a year n 1 in 200 social messages are spam n 5% of all social apps are spammy
5 Spam in Social Networks 5 n What s different about social networks? n Spammers have more ways to interact with users
6 Spam in Social Networks 6 n What s different about social networks? n Spammers have more ways to interact with users n Messages, comments on photos, winks,
7 Spam in Social Networks 7 n What s different about social networks? n Spammers have more ways to interact with users n Messages, comments on photos, winks, n They can split spam across multiple messages
8 Spam in Social Networks 8 n What s different about social networks? n Spammers have more ways to interact with users n Messages, comments on photos, winks, n They can split spam across multiple messages n More available info about users on their profiles!
9 Spammers are getting smarter! 9 Traditional Spam: George Want some replica luxury watches? Click here: Shobeir
10 Spammers are getting smarter! 10 Traditional Spam: George Want some replica luxury watches? Click here: [Report Spam] Shobeir
11 Spammers are getting smarter! 11 Traditional Spam: (Intelligent) Social Spam: George Want some replica luxury watches? Click here: Mary Hey Shobeir! Nice profile photo. I live in Bay Area too. Wanna chat?! [Report Spam] Shobeir Shobeir
12 Spammers are getting smarter! 12 Traditional Spam: (Intelligent) Social Spam: George Want some replica luxury watches? Click here: Mary Hey Shobeir! Nice profile photo. I live in Bay Area too. Wanna chat?! [Report Spam] Shobeir Sure! :) Shobeir
13 Spammers are getting smarter! 13 Traditional Spam: (Intelligent) Social Spam: George Want some replica luxury watches? Click here: Mary Hey Shobeir! Nice profile photo. I live in Bay Area too. Wanna chat?! [Report Spam] Shobeir Sure! :) Realistic Looking Conversation Shobeir I m logging off here., too many people pinging me! I really like you, let s chat more here: Mary
14 Tagged.com 14 n Founded in 2004, is a social networking site which connects people through social interactions and games n Over 300 million registered members n Data sample for experiments (on a laptop): n 5.6 Million users (3.9% Labeled Spammers) n 912 Million Links
15 Social Networks: Multi-relational and Time-Evolving 15 t(1) t(2) t(6) t(5) t(10) t(4) t(8) t(7) t(11) t(3) t(9)
16 Social Networks: Multi-relational and Time-Evolving 16 Legitimate users t(1) t(2) t(6) t(5) t(10) t(4) t(8) t(7) t(11) t(3) t(9)
17 Social Networks: Multi-relational and Time-Evolving 17 Legitimate users Spammers t(6) t(1) t(5) t(2) t(10) t(4) t(8) t(7) t(11) t(3) t(9)
18 Social Networks: Multi-relational and Time-Evolving 18 Legitimate users Spammers t(6) t(1) t(5) t(2) t(10) t(4) t(8) t(7) t(11) t(3) t(9) Link = Action at time t Actions = Profile view, message, poke, report abuse, etc
19 Social Networks: Multi-relational and Time-Evolving 19 t(1) t(2) t(6) t(5) t(10) t(4) t(8) t(7) t(11) t(3) t(9) Link = Action at time t Actions = Profile view, message, poke, report abuse, etc
20 Social Networks: Multi-relational and Time-Evolving 20 Profile view t(1) t(2) t(6) t(5) t(10) t(4) t(8) t(7) t(11) t(3) t(9) Link = Action at time t Actions = Profile view, message, poke, report abuse, etc
21 Social Networks: Multi-relational and Time-Evolving 21 Profile view Message t(1) t(2) t(6) t(5) t(10) t(4) t(8) t(7) t(11) t(3) t(9) Link = Action at time t Actions = Profile view, message, poke, report abuse, etc
22 Social Networks: Multi-relational and Time-Evolving 22 Profile view Message t(1) t(2) t(6) t(5) t(10) t(4) t(8) t(7) t(11) t(3) t(9) Link = Action at time t Poke Actions = Profile view, message, poke, report abuse, etc
23 Social Networks: Multi-relational and Time-Evolving 23 Profile view Message t(1) t(2) t(6) t(5) t(10) Report spammer t(4) t(8) t(7) t(11) t(3) t(9) Link = Action at time t Poke Actions = Profile view, message, poke, report abuse, etc
24 Our Approach 24 Predict spammers based on: n Graph structure n Action sequences n Reporting behavior t(6) t(1) t(5) t(2) t(10) t(4) t(8) t(7) t(11) t(3) t(9)
25 Our Approach 25 Predict spammers based on: n Graph structure n Action sequences n Reporting behavior t(6) t(1) t(5) t(2) t(10) t(4) t(8) t(7) t(11) t(3) t(9)
26 26 Graph Structure Feature Extraction Are you interested? Meet Me Play Pets Friend Request Message Graphs for each relation Wink Report Abuse Pagerank, K-core, Graph coloring, Triangle count, Connected components, In/out degree
27 27 Graph Structure Feature Extraction Features Are you interested? Meet Me Play Pets Friend Request Message Graphs for each relation Wink Report Abuse Pagerank, K-core, Graph coloring, Triangle count, Connected components, In/out degree
28 Graph Structure Features 28 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
29 Graph Structure Features 29 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
30 Graph Structure Features 30 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
31 Graph Structure Features 31 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
32 Graph Structure Features 32 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
33 Graph Structure Features 33 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
34 Graph Structure Features 34 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
35 Graph Structure Features 35 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core X n Graph coloring n Connected components n Triangle count (8 features for each of 10 relations)
36 Graph Structure Features 36 n Extract features for each relation graph es for each of 10 rel n PageRank n Degree statistics n Total degree n In degree n Out degree n k-core n Graph coloring n Connected components n Triangle count X n Viewing profile n Friend requests n Message n Luv n Wink n Pets game n Buying n Wishing n MeetMe game n Yes n No n Reporting abuse (8 features for each of 10 relations)
37 Graph Structure Features 37 Viewing profile Reporting abuse t(1) t(9) t(10) Graph Structure PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring Classification method: Gradient Boosted Trees
38 Graph Structure Features 38 Experiments AU- PR AU- ROC 1 Rela'on, 8 Feature types 10 Rela'ons, 1 Feature type 10 Rela'ons, 8 Feature types ± ± ± ± ± ± Multiple relations/features better performance!
39 Graph Structure Features 39 Experiments AU- PR AU- ROC 1 Rela6on, 8 Feature types 10 Rela'ons, 1 Feature type 10 Rela'ons, 8 Feature types ± ± ± ± ± ± Multiple relations/features better performance!
40 Graph Structure Features 40 Experiments AU- PR AU- ROC 1 Rela'on, 8 Feature types 10 Rela6ons, 1 Feature type 10 Rela'ons, 8 Feature types ± ± ± ± ± ± Multiple relations/features better performance!
41 Graph Structure Features 41 Experiments AU- PR AU- ROC 1 Rela'on, 8 Feature types 10 Rela'ons, 1 Feature type 10 Rela6ons, 8 Feature types ± ± ± ± ± ± Multiple relations/features better performance!
42 Our Approach 42 Predict spammers based on: n Graph structure n Action sequences n Reporting behavior t(6) t(1) t(5) t(2) t(10) t(4) t(8) t(7) t(11) t(3) t(9)
43 Sequence of Actions 43 n Sequential Bigram Features: Short sequence segment of 2 consecutive actions, to capture sequential information User1 Ac'ons: Message, Profile_view, Message, Friend_Request,.
44 Sequence of Actions 44 n Mixture of Markov Models (MMM): A.k.a. chain-augmented, tree-augmented naive Bayes y x 1 x... 2 x n-1 x n ny P (y, x) =P (y)p (x 1 y) P (x i x i i=2 1,y),
45 Sequence of Actions 45 t(1) t(10) t(9) Action Sequence Bigram Features + Chain Augmented NB
46 Sequence of Actions 46 Experiments AU- PR AU- ROC Bigram Features ± ± MMM ± ± Bigram + MMM ± ± Little benefit from MMM (although little overhead)
47 Results 47 Precision-Recall ROC We can classify 70% of the spammers that need manual labeling with about 90% accuracy
48 Deployment and Example Runtimes 48 n We can: n Run the model on short intervals, with new snapshots of the network n Update the features as events occur n Example runtimes with Graphlab Create TM on a Macbook Pro: n 5.6 million vertices and 350 million edges: n PageRank: 6.25 minutes n Triangle counting: minutes n k-core: 14.3 minutes
49 Our Approach 49 Predict spammers based on: n Graph structure n Action sequences n Reporting behavior t(6) t(1) t(5) t(2) t(10) t(4) t(8) t(7) t(11) t(3) t(9)
50 Refining the abuse reporting systems 50 n Abuse report systems are very noisy n People have different standards n Spammers report random people to increase noise n Personal gain in social games n Goal is to clean up the system using: n Reporters previous history n Collective reasoning over reports
51 Collective Classification with Reports 51 t(1) t(10) t(9) Report Subgraph Probabilistic Soft Logic
52 HL-MRFs & Probabilistic Soft Logic (PSL) Probabilistic Soft Logic (PSL), a declarative modeling language based on first-order logic Weighted logical rules define a probabilistic graphical model:! : P (A, B) ^ Q(B,C)! R(A, C) Instantiated rules reduce the probability of any state that does not satisfy the rule, as measured by its distance to satisfaction 52
53 Collective Classification with Reports 53 n Model using only reports: REPORTED(v 1,v 2 )! SPAMMER(v 2 ) SPAMMER(v)
54 Collective Classification with Reports 54 n Model using reports and credibility of the reporter: CREDIBLE(v 1 ) ^ REPORTED(v 1,v 2 )! SPAMMER(v 2 ) PRIOR-CREDIBLE(v)! CREDIBLE(v) PRIOR-CREDIBLE(v)! CREDIBLE(v) SPAMMER(v)
55 Collective Classification with Reports 55 n Model using reports, credibility of the reporter, and collective reasoning: CREDIBLE(v 1 ) ^ REPORTED(v 1,v 2 )! SPAMMER(v 2 ) SPAMMER(v 2 ) ^ REPORTED(v 1,v 2 )! CREDIBLE(v 1 ) SPAMMER(v 2 ) ^ REPORTED(v 1,v 2 )! CREDIBLE(v 1 ) PRIOR-CREDIBLE(v)! CREDIBLE(v) PRIOR-CREDIBLE(v)! CREDIBLE(v) SPAMMER(v)
56 Results of Classification Using Reports 56 Experiments AU- PR AU- ROC Reports Only ± ± Reports & Credibility ± ± Reports & Credibility & Collec've Reasoning ± ± 0.004
57 Results of Classification Using Reports 57 Experiments AU- PR AU- ROC Reports Only ± ± Reports & Credibility ± ± Reports & Credibility & Collec've Reasoning ± ± 0.004
58 Results of Classification Using Reports 58 Experiments AU- PR AU- ROC Reports Only ± ± Reports & Credibility ± ± Reports & Credibility & Collec've Reasoning ± ± 0.004
59 Results of Classification Using Reports 59 Experiments AU- PR AU- ROC Reports Only ± ± Reports & Credibility ± ± Reports & Credibility & Collec6ve Reasoning ± ± 0.004
60 Conclusion 60 t(1) t(10) Graph Structure PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring Multiple relations are more predictive than multiple features AUPR: t(9) Code and part of the data will be released soon:
61 Conclusion 61 t(1) t(10) Graph Structure PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring Multiple relations are more predictive than multiple features AUPR: t(9) Action Sequence Bigram Features + Chain Augmented NB Even simple bigrams are highly predictive AUPR: Code and part of the data will be released soon:
62 Conclusion 62 t(1) t(10) Graph Structure PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring Multiple relations are more predictive than multiple features AUPR: Can classify 70% of the spammers that needed manual labeling with 90% accuracy t(9) Action Sequence Bigram Features + Chain Augmented NB Even simple bigrams are highly predictive AUPR: AUPR: Code and part of the data will be released soon:
63 Conclusion 63 t(1) t(10) Graph Structure PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring Multiple relations are more predictive than multiple features AUPR: Can classify 70% of the spammers that needed manual labeling with 90% accuracy t(9) Action Sequence Bigram Features + Chain Augmented NB Even simple bigrams are highly predictive AUPR: AUPR: Report Subgraph Probabilistic Soft Logic Jointly refining the credibility of the source is highly effective! AUPR: Code and part of the data will be released soon:
64 Acknowledgements 64 n Collaborators: Lise Getoor Univ. California, Santa Cruz Shobeir Fakhraei Univ. of Maryland Madhusudana Shashanka if(we) Inc., currently Niara Inc. n If(we) Inc. (Formerly Tagged Inc.): Johann Schleier-Smith, Karl Dawson, Dai Li, Stuart Robinson, Vinit Garg, and Simon Hill n Dato (Formerly Graphlab): Danny Bickson, Brian Kent, Srikrishna Sridhar, Rajat Arya, Shawn Scully, and Alice Zheng
65 Conclusion 65 t(1) t(10) Graph Structure PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring PageRank Triangle Count Out-Degree In-Degree k-core Graph Coloring Multiple relations are more predictive than multiple features AUPR: Can classify 70% of the spammers that needed manual labeling with 90% accuracy t(9) Action Sequence Bigram Features + Chain Augmented NB Even simple bigrams are highly predictive AUPR: AUPR: Report Subgraph Probabilistic Soft Logic Jointly refining the credibility of the source is highly effective! AUPR: Code and part of the data will be released soon:
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