Post on 20-Jan-2016
description
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