Statistical Analysis of the Social Network and DiscussionThreads in Slashdot

Vicenç Gómez, Andreas Kaltenbrunner, Vicente López

Defended by: Alok Rakkhit

Goals

Understand underlying pattern of communication

Lead towards efficient techniques to improve system performance

Evaluate Controversy of a thread

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)

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

Topology Types

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

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

Degree Distribution

Effects of Score

Calculated mean score of users with at least 10 postsFound two classes of writers: good and

average Good writers

Bias in number of comments receivedMore replies to their poorly scored posts than

those of average users

Community Structure:

Most pairs have few commentsFew have very high, up to 108

Good writers form backbone of network.

Agglomerative Clustering

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 articlesBranching for level 0 bell shaped, others have

continuous decrease (LN fit)

RADIAL TREES

Branching Factors

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

Impact

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