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Decomposing discussion forums using user roles
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Decomposing discussion forums using user roles



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  • 1. Decomposing discussion forums using user roles Jeffrey Chan & Conor Hayes
    Friday seminar
    Presenter: AstaZelenkauskaite
  • 2. Feature-based profiling
    Users roles are identified by the features (indicators) to profile user behavior
    Visualization techniques
    Downside: used only for small-scale studies
    Proposed solution: soc net analysis
    Ego-network analysis and the out-degree distribution
  • 3. Data – the largest discussion board in Ireland
    596 forums
    75400 users
    244850 threads
    4.3 mln posts
  • 4. Forums
  • 5. Optimal number of clusters
  • 6. Analysis
    Weighted directed graph
    Ego-net graph – reply graph (multi-edge graph)
    20 forums from 01/07/2006 – 31/12/2006.
  • 7. Features
    Initially 50 features, redundant eliminated
    Structural features (as communication btw users)
    Unweighted directed graphs
    From interaction with their neighbors
    Reciprocity features
    Persistence features
    Popularity features
    Initialization features
  • 8. Structural features (operationalization)
    From interaction with their neighbors
    Reciprocity features
    % of bi-directional neighbors (represents the % of the neighbors of a user where there is both in and out edges – they have replied to each other).
    Persistence features
    The length of the conversations a user typically engages in (mean and sd of the posts per thread).
    Popularity features
    Ratio of a users’ in-neighbors (% of in-degree) # of replies
    % of the posts where there is at least one reply to the user.
    Initialization features
    Initiated % of msgsby a user.
  • 9. User role discovery approach
    Data cleaning
    Filtering out low-degree, low posting users
    User grouping
    Via number of neighbors
  • 10. User roles
    Joining conversationalists
    the ones who do not initiate but post replies
    Low reciprocity (rarely get involved into two-way communication)
    Low % of neighbors w/ two-way communication
    Middle range of the statistics of all features
    Popular participant
    Do not initiate many threads but get involved with a large percentage of users of a forum
    Similar to taciturns, relatively high levels of reciprocity.
    Extremely low % posts being replied to (not very popular)
  • 11. clusters
  • 12.
  • 13. Results: Forum composition
    Some forums are distinctively different from the others (eg. personal issues)
    Difference in grouping by conversationalists vstaciturns
    Some topics determine certain composition
  • 14. Discussion
    Is it impossible to assess the ‘success of functioning’ from the composition of the group?