1. Statistical Analysis of the Social Network and Discussion Threads in Slashdot Vicenç Gómez, Andreas Kaltenbrunner, Vicente López Defended by: Alok Rakkhit

2. Goals Understand underlying pattern of communication Lead towards efficient techniques to improve system performance Evaluate Controversy of a thread

3. Why Slashdot? Community-based moderation of message boards Scoring system Thread comments mainly respond to each other rather than to article Same dataset as previous studies (characterizing its size and lifespan)

4. Network Structure Filtered out  Original Poster (if no other involvement)  Self-replies  Anonymous posts  -1 scores Topology created in 3 ways  Undirected Dense  Undirected Sparse  Directed

5. Topology Types

6. Network Structure - Expected Features One giant cluster containing vast majority of users Isolated clusters of two to four  Two orders of magnitude above random Small path lengths Small maximum distance

7. Degree Analysis High variance Degree coefficient very small  Major diff from traditional social networks Moderate reciprocity Tail of distribution not authors of posts Truncated Log-Normal (LN) hypothesis formed much better approximation than Power-Law hypothesis

8. Degree Distribution

9. Effects of Score Calculated mean score of users with at least 10 posts  Found two classes of writers: good and average Good writers  Bias in number of comments received  More replies to their poorly scored posts than those of average users

10. Community Structure: Most pairs have few comments  Few have very high, up to 108 Good writers form backbone of network.

11. Agglomerative Clustering

12. Discussion structure: Radial tree representation used High heterogeneity in shape Similar mechanism behind their evolution  Broad first level, wider second level, followed by exponential decay Decay due to accessibility, new articles  Branching for level 0 bell shaped, others have continuous decrease (LN fit)

13. RADIAL TREES

14. Branching Factors

15. Evaluating Controversy Little work done in area  Other available method involves training a classifier for semantic and structural analysis Propose using an h-index  modified from paper output of researchers  Simple, based of structure alone  Factors both number of comments and maximum depth  Tie breaker to thread with fewer comments

16. Impact Cited by 11 papers Automatic scoring of posts Predicting popularity of online content What makes conversations interesting Comparing volume vs. interaction