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Controversial Users demand
      Local Trust Metrics: an
           Experimental
      Study on Epinions.com
            Community
                                                 
                                    Paolo Massa
                             PhD student in ICT
                     ITC/iRST and University of Trento
                    Blog: http://moloko.itc.it/paoloblog/
                      (Joint Work with Paolo Avesani)
                  (Thanks Epinions.com for providing data)
                                       
      Slides licenced under CreativeCommons Attribution­ShareAlike (see last slide for more info)
Summary

■What is Epinions.com

■Trust Networks and Trust Metrics

     
         Local vs Global Trust Metrics
■Controversial Users

■Experiments and Results

■Conclusions


                          
Motivation

        In a society, some peers are unknown to you (ebay, 
        p2p, ...)

        Q: “Should I trust peer A?” [decentralization­­>relevant]

        Most papers assume a peer has a unique quality value 
        (there are good peers and bad peers, goal is to spot bad)

        IRREALISTIC assumption (Evidence from real online  
        community of 150.000 users).

        Consequence: we need Local Trust Metrics 
     
        (personalized) [But most papers propose Global Metrics]
                                   
Epinions.com

    What is Epinions.com?
    
        Community web site where users can
        
            Write reviews about items and give them ratings
        
            Express their Web of Trust (“Users whose reviews and 
            ratings you have consistently found to be valuable”)
        
            Express their Block List (“Users whose reviews and 
            ratings ... offensive, inaccurate, or in general not valuable”)
    
        Reviews of TRUSTed users are more visible
    
        Reviews of DISTRUSTed users are hidden
                                         
Epinions.com
                   Dr.P profile page

                Dr.P's Web of Trust
               (Block List is hidden)



                          Do you 
                   trust or distrust Dr.P?




                   Ratings given by Dr.P
          
Real uses of Trust
News sites: Slashdot.org, Kuro5hin.org, ...

E­marketplaces: Ebay.com, Epinions.com, Amazon.com, ...

P2P networks: eDonkey, Gnutella, JXTA

Jobs sites: LinkedIn, Ryze, ...

Friendster, Tribes, Orkut and other “social” sites.

Opensource Developers communities: Advogato.org (Affero.org)

Hospitalityclub.org, couchsurfing.com: hosting in your house unknown people?

Bookcrossing and lending stuff sites.

Network of personal weblogs (the blogroll is your trust list)

Semantic Web: FOAF (Friend­Of­A­Friend) is an RDF format that allows to express social 
  relationships (~10 millions files) and XFN microformat
                                               
PageRank (Google) ... MyWeb2.0 (Yahoo!)
Trust networks (are graphs)
    
        Aggregate all the trust statements to produce a 
        trust network.              A node is a user (example: Dr.P).
                                             A direct edge is a trust statement
                           0
                    Mena             Ben                           In Epinions, 
        0.2                                   Properties of Trust: just 1 and 0!
              0.9                               ­ weighted (0=distrust, 1=max trust)
                               0.6
                                                ­ subjective
                1
    Dr.P             Doc                        ­ asymmetric ­ context­dependent

                                     Trust Metric (TM):
                      ?              Uses existing edges for predicting values
                                     of trust for non­existing edges, 
                                      thanks to trust propagation (if you trust 
                                     someone, then you have some degree of 
                                             
                                     trust in anyone that person trusts).
TM perspective: Local or Global
                         1                1
            Mary               Mena                Bill
                                                           How much Bill can be trusted?
                                          0                 On average (by the community)?
                                                            By Mary?
              ME         1
                                Doc                         And by ME?


        Global Trust Metrics:
    
            “Reputation” of user is based on number and quality of incoming edges. Bill has 
            just one predicted trust value (0.5).
    
            PageRank (Google), eBay, Slashdot, ... Works badly for controversial people


        Local Trust Metrics
    
            Trust is subjective ­­> consider personal views (trust “Bill”?)
                                                    
    
            Local can be more effective if people are not standardized.
Controversial Users

    
        Intuitively: a Controversial User is
        
            TRUSTED by some users
        
            DISTRUSTED by some users


    
        Do you want an example?




                                  
Controversial Users: an example
        1                                 0
        1                                 0
        1                                 0
        1                                 0
        1                                 0

       (....)                             (....)
        1                                 0

       100M people                 100M people

If you don't know Bush, should you trust Bush? 
T(Bush)=0.5? Make sense? Here global metrics don't.
                          
Controversial Users: an example
              1                                 0
              1                                 0
              1                                 0
                                                           1
     1        1                                 0
R             1                                 0
                                                               D
     1                                                     1
             (....)                            (....)
              1                                 0

             100M people                  100Mpeople
Local Metric makes more sense. Your trust in Bush 
 depends on your trusted users!
                                  
T(R,Bush)=1                                  T(D,Bush)=0
Controversial Users on Epinions

        Controversial users are normal in societies
        
            How many controversial users on Epinions.com?
But first, two definitions of Controversiality:

        Controversiality Level of A: number of users that 
        disagree with the majority = Min(#trust, #distrust)

        Contr. “Percentage” of A = (T­D) / (T+D) in [­1, 1]
        
            CP(A)=1   if A is trusted by everyone (loved!)
        
            CP(A)=­1  if A is distrusted by everyone (hated!)

     
        
            CP(A)=0   if A is trusted by n users and distrusted by n users
                                         
Experiment

        Epinions.com dataset
        
            Real Users: ~150K
        
            Edges (Trust / Distrust): 841K (717K / 124K)

        
            ~85K received at least one judgement (trust or distrust)
        
            17.090 (>20%) are at least 1­controversial (at least 1 user 
            disagrees with the majority)  ­­>  Non negligible portion!
        
            1.247 are at least 10­controversial
        
            144 are at least 40­controversial
     
        
            1 user is 212­controversial! (~400 trust her, 212 distrust her)
                                         
Experiment
 
         Comparing 2 metrics about accuracy in trust/distrust 
         prediction.
         
             Global: ebay­like. Trust(A)=#trust/(#trust+#distrust)
         
             Local: MoleTrust, based on Trust Propagation from current 
             user (simple and fast)

Cycles are a problem ­­> Order peers 
  based on distance from source user
   Trust of users at level k is based only 
     on trust of users at level k­1 (and k)
   Trust propagation horizon & decay
                                         
Experiment

        How do we compare metrics?

        Leave­one­out: Remove an edge in Trust Network and 
        try to predict it. Then compute error as absolute 
        difference between Real and Predicted value.
        
            Also differentiating over trust or distrust statements




                                         
Exp. on Controversiality Level
                                             y=error made by TM predicting 
                                              edges on users with x 
Error




                                              controversiality level.


                                            Predicting Distrust is more 
            Ebay Controversiality level
                                              difficult.
                                             Ebay error on Distrust ~ 0.6
                                             Mole2 error on Distrust ~ 0.4
Error




                                            Error on Trust is similar because 
                                              (#trust >> #distrust)
                                             
        MoleTrust2 Controversiality level
Exp. on Controversiality Percentage
                                           CP~0 = Controversial User
                                           Error Ebay = 0.5 on Contr.Us
Error




                                           Error MoleTrust2 smaller 
                                            but not as small as we 
        Ebay Controversiality percentage    would like: can we reach 0?
                                           Other experiments in paper:
                                                Error on Trust Edges.
Error




                                                Error on Distrust Edges (very 
                                                  important to correctly predict 
                                                  these ones!)
 MoleTrust2 Controversiality percentage
Other experiments

    
        MoleTrust with different propagation horizons
        
            2, 3, 4
    
        Computing Coverage.




                                 
Conclusions

        In complex societies, it is normal that someone likes 
        you and someone dislikes you.
        
            Most Papers make assumption of unique quality 
            value for a peer (and propose an algo for predicting it)
        
            This is IRREALISTIC! (I know this is intuitive but 
            still ...)




                                      
Conclusions (2)

        As a consequence, we need Local Trust Metrics.
        
            Local TMs are computationally much more expensive than 
            Global TMs! ­­> Possibly, you run it locally for yourself on 
            your mobile or on your browser (should be fast!)
        
            Local TMs exploits less information ­­> reduced coverage.
        
            Global Metrics fine in non­controversial domains: possibly 
            ok on Ebay, surely not ok on sites about (political?) opinions 

        Trust networks are everywhere!

        More research is needed: Yahoo! (with MyWeb2.0) 
        and Google are there. More real testbeds, more 
     
        proposals of Local TMs, more comparisons, ...
                                  
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The End



            The End.
    Thanks for your attention!


         Questions?
                    

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Controversial Users demand Local Trust Metrics: an Experimental Study on Epinions.com Community

  • 1. Controversial Users demand Local Trust Metrics: an Experimental Study on Epinions.com Community   Paolo Massa PhD student in ICT ITC/iRST and University of Trento Blog: http://moloko.itc.it/paoloblog/ (Joint Work with Paolo Avesani)   (Thanks Epinions.com for providing data)   Slides licenced under CreativeCommons Attribution­ShareAlike (see last slide for more info)
  • 2. Summary ■What is Epinions.com ■Trust Networks and Trust Metrics  Local vs Global Trust Metrics ■Controversial Users ■Experiments and Results ■Conclusions    
  • 3. Motivation  In a society, some peers are unknown to you (ebay,  p2p, ...)  Q: “Should I trust peer A?” [decentralization­­>relevant]  Most papers assume a peer has a unique quality value  (there are good peers and bad peers, goal is to spot bad)  IRREALISTIC assumption (Evidence from real online   community of 150.000 users).  Consequence: we need Local Trust Metrics    (personalized) [But most papers propose Global Metrics]  
  • 4. Epinions.com What is Epinions.com?  Community web site where users can  Write reviews about items and give them ratings  Express their Web of Trust (“Users whose reviews and  ratings you have consistently found to be valuable”)  Express their Block List (“Users whose reviews and  ratings ... offensive, inaccurate, or in general not valuable”)  Reviews of TRUSTed users are more visible  Reviews of DISTRUSTed users are hidden    
  • 5. Epinions.com Dr.P profile page Dr.P's Web of Trust (Block List is hidden) Do you  trust or distrust Dr.P? Ratings given by Dr.P    
  • 6. Real uses of Trust News sites: Slashdot.org, Kuro5hin.org, ... E­marketplaces: Ebay.com, Epinions.com, Amazon.com, ... P2P networks: eDonkey, Gnutella, JXTA Jobs sites: LinkedIn, Ryze, ... Friendster, Tribes, Orkut and other “social” sites. Opensource Developers communities: Advogato.org (Affero.org) Hospitalityclub.org, couchsurfing.com: hosting in your house unknown people? Bookcrossing and lending stuff sites. Network of personal weblogs (the blogroll is your trust list) Semantic Web: FOAF (Friend­Of­A­Friend) is an RDF format that allows to express social  relationships (~10 millions files) and XFN microformat     PageRank (Google) ... MyWeb2.0 (Yahoo!)
  • 7. Trust networks (are graphs)  Aggregate all the trust statements to produce a  trust network. A node is a user (example: Dr.P). A direct edge is a trust statement 0 Mena Ben In Epinions,  0.2 Properties of Trust: just 1 and 0! 0.9 ­ weighted (0=distrust, 1=max trust) 0.6 ­ subjective 1 Dr.P Doc  ­ asymmetric ­ context­dependent Trust Metric (TM): ? Uses existing edges for predicting values of trust for non­existing edges,   thanks to trust propagation (if you trust  someone, then you have some degree of      trust in anyone that person trusts).
  • 8. TM perspective: Local or Global 1 1 Mary Mena Bill How much Bill can be trusted? 0  On average (by the community)?  By Mary? ME 1 Doc  And by ME?  Global Trust Metrics:  “Reputation” of user is based on number and quality of incoming edges. Bill has  just one predicted trust value (0.5).  PageRank (Google), eBay, Slashdot, ... Works badly for controversial people  Local Trust Metrics  Trust is subjective ­­> consider personal views (trust “Bill”?)      Local can be more effective if people are not standardized.
  • 9. Controversial Users  Intuitively: a Controversial User is  TRUSTED by some users  DISTRUSTED by some users  Do you want an example?    
  • 10. Controversial Users: an example 1 0 1 0 1 0 1 0 1 0 (....) (....) 1 0 100M people 100M people If you don't know Bush, should you trust Bush?  T(Bush)=0.5? Make sense? Here global metrics don't.    
  • 11. Controversial Users: an example 1 0 1 0 1 0 1 1 1 0 R 1 0 D 1 1 (....) (....) 1 0 100M people 100Mpeople Local Metric makes more sense. Your trust in Bush  depends on your trusted users!     T(R,Bush)=1                                  T(D,Bush)=0
  • 12. Controversial Users on Epinions  Controversial users are normal in societies  How many controversial users on Epinions.com? But first, two definitions of Controversiality:  Controversiality Level of A: number of users that  disagree with the majority = Min(#trust, #distrust)  Contr. “Percentage” of A = (T­D) / (T+D) in [­1, 1]  CP(A)=1   if A is trusted by everyone (loved!)  CP(A)=­1  if A is distrusted by everyone (hated!)    CP(A)=0   if A is trusted by n users and distrusted by n users  
  • 13. Experiment  Epinions.com dataset  Real Users: ~150K  Edges (Trust / Distrust): 841K (717K / 124K)  ~85K received at least one judgement (trust or distrust)  17.090 (>20%) are at least 1­controversial (at least 1 user  disagrees with the majority)  ­­>  Non negligible portion!  1.247 are at least 10­controversial  144 are at least 40­controversial    1 user is 212­controversial! (~400 trust her, 212 distrust her)  
  • 14. Experiment  Comparing 2 metrics about accuracy in trust/distrust  prediction.  Global: ebay­like. Trust(A)=#trust/(#trust+#distrust)  Local: MoleTrust, based on Trust Propagation from current  user (simple and fast) Cycles are a problem ­­> Order peers  based on distance from source user Trust of users at level k is based only  on trust of users at level k­1 (and k) Trust propagation horizon & decay    
  • 15. Experiment  How do we compare metrics?  Leave­one­out: Remove an edge in Trust Network and  try to predict it. Then compute error as absolute  difference between Real and Predicted value.  Also differentiating over trust or distrust statements    
  • 16. Exp. on Controversiality Level  y=error made by TM predicting  edges on users with x  Error controversiality level. Predicting Distrust is more  Ebay Controversiality level difficult.  Ebay error on Distrust ~ 0.6  Mole2 error on Distrust ~ 0.4 Error Error on Trust is similar because  (#trust >> #distrust)     MoleTrust2 Controversiality level
  • 17. Exp. on Controversiality Percentage CP~0 = Controversial User Error Ebay = 0.5 on Contr.Us Error Error MoleTrust2 smaller  but not as small as we  Ebay Controversiality percentage would like: can we reach 0? Other experiments in paper: Error on Trust Edges. Error Error on Distrust Edges (very  important to correctly predict      these ones!) MoleTrust2 Controversiality percentage
  • 18. Other experiments  MoleTrust with different propagation horizons  2, 3, 4  Computing Coverage.    
  • 19. Conclusions  In complex societies, it is normal that someone likes  you and someone dislikes you.  Most Papers make assumption of unique quality  value for a peer (and propose an algo for predicting it)  This is IRREALISTIC! (I know this is intuitive but  still ...)    
  • 20. Conclusions (2)  As a consequence, we need Local Trust Metrics.  Local TMs are computationally much more expensive than  Global TMs! ­­> Possibly, you run it locally for yourself on  your mobile or on your browser (should be fast!)  Local TMs exploits less information ­­> reduced coverage.  Global Metrics fine in non­controversial domains: possibly  ok on Ebay, surely not ok on sites about (political?) opinions   Trust networks are everywhere!  More research is needed: Yahoo! (with MyWeb2.0)  and Google are there. More real testbeds, more    proposals of Local TMs, more comparisons, ...  
  • 21. Licence of this slides These slides are released under Creative Commons Attribution­ShareAlike 2.5 You are free:     * to copy, distribute, display, and perform the work     * to make derivative works     * to make commercial use of the work Under the following conditions: Attribution. You must attribute the work in the manner specified by the author or licensor. Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to  this one.     * For any reuse or distribution, you must make clear to others the license terms of this work.     * Any of these conditions can be waived if you get permission from the copyright holder. Your fair use and other rights are in no way affected by the above.     More info at http://creativecommons.org/licenses/by­sa/2.5/
  • 22. The End The End. Thanks for your attention! Questions?