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Expertise Networks in Online Communities: Structure and Algorithms. Jun Zhang Mark S. Ackerman Lada Adamic University of Michigan WWW 2007, May 8–12, 2007, Banff, Alberta, Canada. Outline. Automatically identify users with expertise. Analysis of the java forum

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expertise networks in online communities structure and algorithms

Expertise Networks in Online Communities: Structure andAlgorithms

Jun Zhang

Mark S. Ackerman

Lada Adamic

University of Michigan

WWW 2007, May 8–12, 2007, Banff, Alberta, Canada.

  • Automatically identify users with expertise.
  • Analysis of the java forum
  • Test various network based ranking algorithms such as HITS and PageRank
  • Use simulations rules to evaluate how other alogorithms perform on Java Forum.
  • Evaluate performance in communities with different characteristics.
Expertise Finder – Systems that help to find others with appropriate expertise to answer a question.

Current Expertise finders – Modern Information retrieval techniques.

Represent as term vector, match expertise queries using standard IR techniques.

Problem : Reflect if a person knows about a topic but does not distinguish person’s relative expertise levels.

Solution – Use network based ranking algorithm + content analysis.

expertise network
Expertise Network
  • Usually have discussion thread structure
  • Not a network focused on social relationships
  • User replies because of interest in content.
  • CEN – Community Expertise Network – Distribution of expertise along with network responses
  • Structural Prestige – Closely related. Receiving more positive choices is prestigious.
empirical study java forum
Empirical Study – Java Forum
  • People come to ask questions.
  • 87 sub forums with large diversity of users.
  • 333,314 messages in 49,888 threads.
  • 13,739 nodes and 55,761 edges.
  • Used human raters and selected 135 users – omitting users postings less than 10 times.
characterizing the network
Characterizing the Network
  • Bow-tie Structure analysis
  • Degree Distribution – To capture Level of interaction.
    • Scale Free - Highly uneven distribution of participation.
  • Degree Correlations
    • Indegree – how many people a given user helps.
    • Does not provide users’ own tendency to provide help- Eg. Only reply to newbies or talk to similar expertise level people.
    • For Each asker-replier count indegree of replier vs asker.
expertise ranking algorithms
Expertise Ranking Algorithms
  • Simple Statistical Measure
    • Answers lot = knows the topic well.
    • Spammers – inflammatory or disruptive posts.
    • Handling Problem
      • Users’ relevance feedback.
    • AnswerNum – No of questions answered.
    • Also count no of users a user helped.
    • Shows broader or greater expertise.
z score measures
Z- Score Measures
  • Replying many = High Expertise
  • Asking many = lacks expertise on topics
  • Z – Score Combines both q + a.
  • Measure how different from a random user
    • Post answers with p = 0.5 so n*p =n/2 replies
    • Std Dev. Sqrt ( n*p*(1-p) = Sqrt(n) / 2
  • Asks and answers ~= 0, Answer more +
expertise rank algorithm
Expertise Rank Algorithm
  • Problem in Counting no posts
    • user answered 100 newbie questions ranked equally expert as 100 advanced users’ ques.
  • Adopt method similar to PageRank.
  • Intuition B<-A and C<- B .C’s Expertise boosted.
  • C(Ui) – Total no of users helping U1
  • d – Damping factor was set to 0.85
  • Could also be weighted including WiA – No times i was helped by A
  • In this study, weighting does not improve the accuracy.
  • 2 raters- Java Programming experts.
  • Five Levels of Expertise Rating.
statistical metrics
Statistical Metrics
  • Frequently used correlation measures
    • Spearmans rho : Does not handle weak ordering(i.e. Multiple items in ranking such that neither item is preferred over the other).
    • Kendall’s Tau : Gives equal weight to any interchange of equal distance, no matter where it occurs. Eg between 1 & 2, 101 &102
    • TopK :Calculates Kendall’s Tau only for highest 20 ranks
  • The Need for it
    • Understanding the human dynamics that shape an online community.
    • This will help select appropriate algorithm for communities where dynamics different from the Java Forum.
  • 2 Models - Best Preferred and Just Better Network
best preferred network
Best Preferred Network
  • Many experts answered others’ questions and seldom asked questions.
  • Very much similar to the Java Forum.

P of replying increases exponentially with expertise level difference between 2 users

just better network
Just Better Network
  • Eg. Within an Organisation, experts may be under time constraints. Choose to answer only questions makes best use of their expertise.
  • Users having slightly better level of expertise answers.
  • U’s probability of answering a’s question
  • Users make best use of their time
  • They are more selective in answering.
  • ExpertiseRank propagates expertise score from newbies to intermediate users who answer their question.
  • From them to experts.
  • In General ExpertiseRank outperforms others.
summary future work
Summary & Future Work
  • Structural Information can be used to evaluate expertise network in online setting.
  • Relative expertise could be found using social network-based algorithms.
  • These algorithms did nearly as well as human raters.
  • In Future, Combine content information – to differentiate specific knowledge and structural information.