Blogosphere: Research Issues, Tools and
Applications
Huan Liu and Nitin Agarwal
{Huan.Liu, Nitin.Agarwal.2}@asu.edu
Computer Science and Engineering
Arizona State University
An updated version could be downloaded from
www.public.asu.edu/~huanliu/KDD08BlogosphereTutorial.pdf or www.public.asu.edu/~nagarwa6/KDD08BlogosphereTutorial.pdf
Acknowledgments
• We would like to express our sincere thanks to Magdiel Oliveras Galan,
John J. Salerno, Shankar Subramanya, Sanjay Sundarajan,Lei Tang, Philip S.
Yu , and Alan Zheng Zhao for collaboration, discussion, and valuable
comments.
• This work is, in part, sponsored by AFOSR and ONR grants in 2008.
• This agreement covers the use of all slides of this tutorial.
– You may use these slides freely for teaching if you send us an email
stating the university name and class/course number in advance, and
cite this tutorial.
– If you wish to use these slides in any other ways, please contact (or
email) us. The ppt version contains notes with additional information
such as various sources in addition to References.
Outline
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Background: Web 2.0 and Social Networks
Blogosphere: Definition, Types, and Comparison
Blogosphere Research Issues
Tools and APIs
Data Collection
Measures, Models, and Methods
Performance, Evaluation, and Metrics
Case Studies
References
WEB 2.0 AND SOCIAL NETWORKS
Web vs. Web 2.0
Characteristics of
Web 2.0
•
•
•
•
•
Rich Internet Applications
User generated contents
User enriched contents
User developed widgets
Collaborative environment: Participatory Web, Citizen
journalism
• Thus, it leverages the power of the Long Tail with user
generated data as the driving force
• More of a paradigm shift than a technology shift
Web 2.0 Services (examples)
•
Blogs
–
–
•
Wikis
–
–
•
Youtube
Flickr
Social Tagging
–
•
Facebook
Myspace
Orkut
Digital media sharing websites
–
–
•
Wikipedia
Wikiversity
Social Networking Sites
–
–
–
•
Blogspot
Wordpress
Del.icio.us
Others
–
–
Twitter
Yelp
Top 20 Most Visited Websites
• Internet traffic report by Alexa on July 29th 2008
1
Yahoo!
11
Orkut
2
Google
12
RapidShare
3
YouTube
13
Baidu.com
4
Windows Live
14
Microsoft Corporation
5
Microsoft Network
15
Google India
6
Myspace
16
Google Germany
7
Wikipedia
17
QQ.Com
8
Facebook
18
EBay
9
Blogger
19
Hi5
10
Yahoo! Japan
20
Google France
• 40% of the top 20 websites are Web 2.0 sites
Social Networks
• A social structure made of nodes (individuals or
organizations) that are related to each other by various
interdependencies like friendship, kinship, like, ...
• Graphical representation
– Nodes = members
– Edges = relationships
Social Networks
Social Networks
• A social structure made of nodes (individuals or
organizations) that are related to each other by various
interdependencies like friendship, kinship, like, ...
• Graphical representation
– Nodes = members
– Edges = relationships
• Various realizations
–
–
–
–
–
Social bookmarking (Del.icio.us)
Friendship networks (facebook, myspace)
Blogosphere
Media Sharing (Flickr, Youtube)
Folksonomies
Some Related CFPs
• ACM TKDD Special Issue on Social Computing
http://www.public.asu.edu/~huanliu/acm-tkdd-sbp
• Second International Conference on Social
Computing, Behavioral Modeling, and
Prediction (SBP09)
http://www.public.asu.edu/~huanliu/sbp09
• SIAM International Conf on Data Mining (SDM)
Sparks (Reno area), Nevada, April 30 - May 2, 2009.
http://www.siam.org/meetings/sdm09
Definitions, Types, and Comparison
BLOGOSPHERE
Blogging Phenomenon
• It’s growing fast as a new means for online
communications and interactions
• A blogger could gain instant fame via his
blogs
• A blogger may make a good living with her
blogs
• Abundant, lucrative business opportunities
• A new political arena
“The site, chock full of advertising,
is a moneymaking machine – so
much so that Ms. Armstrong and
her husband have both quit their
regular jobs.“
The reason? The advertisers are
eager to influence her 850,000
readers.
Arnold Kim, founder and senior editor of
MacRumors.com.
“The site places MacRumors No. 2 on a list
of the ‘25 most valuable blogs,’ …” What is
the potential value? “Two of the other techoriented blogs on its list, …, were sold
earlier this year, reportedly for sums in
excess of $25 million.”
Source: The New York Times
Blogosphere Growth
• “In January 2004, there were about 1 million blogs on the
Internet. As of mid-2006, the population of the ‘blogosphere’
was well past 50 million and climbing.” – Paul Gillin, The New
Influencers, 2007
“36 million women participate
in the blogosphere each
week, and 15 million have
their own blogs”
– A Study by BlogHer
Today Front Page NY Times
The Year of the Political
Blogger Has Arrived
… both parties understand
the need to have greater
numbers of bloggers attend.
… to bring down the walls of
the convention …
Understanding Blogosphere
•
•
•
•
•
Blogosphere
Blog sites
Bloggers
Blog posts
Reverse chronologically
ordered entries
• Blogroll
• Permalinks
• Trackback
• Everyone can publish,
but few are heard
• Many interesting
questions to address
– How to build traffic
– How to find niche online
– How to increase
influence
– How to …
• Fertile research domain
Blog Site
Blog Post
Blogger
Types of Blogs
• Individual vs. community
– Single authored (Individual blog sites)
– Multi authored (Community blog sites)
Individual Blog Sites
Community Blog Sites
Owned and maintained by individual users.
Owned and maintained by a group of like-minded
users.
More like personal accounts, journals or diaries.
More like discussion forums and discussion
boards.
No or almost negligible group interaction.
High degree of group discussion and
collaboration.
No or almost negligible collective wisdom.
Enormous collective wisdom and open source
intelligence.
• Regulated vs. anonymous
Blogosphere
• Complex Social Networks
• Vertices (Nodes): Bloggers/
Blog posts/Blog sites
• Edges: Relationships/Links
• In-Degree: Number of
inlinks
• Out-Degree: Number of
outlinks
Friendship Networks vs. Blogosphere
Friendship Networks
Blogosphere
Explicit Links/Edges Implicit Links/Edges
Undirected Graph Directed Graph
Network Centrality Measures Blog Statistics
Quantifying Spread of Influence Quantifying Influential Members
Nodes are members/actors Nodes can be bloggers/blogs or blog sites
Strictly defined graph structure Loosely defined graph structure
“Being in touch” or “Making Friends” Sharing ideas and opinions
Person-to-person Person-to-group
Friendship Oriented Community Oriented
Member’s Reputation/Trust based on network Member’s Reputation/Trust based on the response
connections and/or location in the network to other member’s knowledge solicitations
Friendship Networks vs. Blogosphere
Social Networks
Orkut, Facebook, LinkedIn,
Classmates.com, etc.
Social
Friendship
Networks
Blogosphere
LiveJournal, MySpace, etc.
TUAW, Blogger, Windows Live
Spaces, etc.
Citation Networks vs. Blogosphere
• Citation links
– DBLP: strict notion of links. People cite what they refer to
– Blogs: links are casual and often missing
• Social networks
– DBLP: inferred from co-authorship, citation networks
– Blogs: people explicitly specify their social network or inferred
from links, comments, etc.
• Communities
– DBLP: conference venues, journals, (relatively static)
– Blogs: community blogs, inferred from blog roll (related blogs),
topic taxonomy, blog-blog interaction, (very dynamic)
BLOGOSPHERE RESEARCH ISSUES
Understanding Blogosphere
• Understand structures and properties of Blogosphere
• Gain insights into the relationships between
bloggers, readers, blog posts, comments, different
blog sites in Blogosphere
• Models help generate artificial data, tune the
parameters to simulate special scenarios, and
compare various studies and different algorithms
• Study peculiarities in Blogosphere and infer latent
patterns and structures that could explain certain
phenomena like influence, diffusion, splogs,
community discovery.
Modeling Web and Blogosphere
• Some key differences between Web and Blogosphere
– Models developed for Web assume dense graph structure due to a large
number of interconnecting hyperlinks within webpages. This assumption does
not hold true. Blogosphere is shown to have a very sparse hyperlink structure
[Kritikopoulos et al. 2006].
– The level of interaction in terms of comments and replies to blog posts makes
Blogosphere different from Web
– The highly dynamic and “short-lived” nature of the blog posts could not be
simulated by the web models. Web models do not consider dynamicity in the
web pages
– Web models assume webpages accumulate links over time. However, this is
not true with Blogosphere
– “Categories” and “tags” gives blogs flexibility that conventional websites
typically don’t have
– Descriptive filenames used in permalinks of blogs as compared to webpage
filenames
Modeling Blogosphere
• Preferential attachment
– Probability of a new edge to a node to be added depends on its degree
– “The rich get richer”
P(e : vi  v j )  deg( vi )
– Power law distribution or scale free distribution
Modeling Blogosphere
• Preferential attachment
– Probability of a new edge to a node to be added depends on its degree
P(e : vi  v j )  deg( vi )
– “The rich get richer”
– Power law distribution or scale free distribution
Modeling Blogosphere
•
Preferential attachment
P(e : vi  v j )  deg( vi ) / V
– Probability of a new edge to a node to be added depends on its degree
– “The rich get richer”
– Power law distribution or scale free distribution
•
Hybrid model
P(e : vi  v j )   deg( vi ) / V  (1   )
– Mixture of both preferential attachment model and random model
– Give a lucky poor guy some chance to get rich
– To solve irreducibility (strong connectedness with few isolated subgraphs) random walk
on a graph model proposes a random jump with a fixed probability
•
Leskovec et al. 2007 studied temporal patterns
–
–
–
–
•
How often people create blog posts
Busrtiness and popularity
How these posts are linked and what is the link density
Developed a SIS based model
Kumar et al. 2003 use blogrolls on the blog posts to construct a network of blog
posts assuming that blogrolls contain similar blog posts
Blog Clustering
Blog Clustering
• Dynamic and automatic organization of the content
• Convenient accessibility
• Optimizing search engines by reducing search space
– Search only the relevant cluster
•
•
•
•
Focused crawling
Summarization
Topic identification
Reduce information overload
– 175,000 blog posts per day, i.e., 2 blog posts per second – Dec
2006
• Extraction and analysis of the trends
Blog Clustering (2)
tfidfi , j  tfi , j  idf i
• Brooks and Montanez 2006, used tf-idf and
picked top 3 keywords for blog posts
idf
tfi , j 
ni , j
n
k
 log
– Clustered blogs based on these keywords
– Reported improved clustering as compared to that using tags
i
d
k, j
D
j
: ti  d j 
• Li et al. 2007 assigned different weights to title, body,
and comments of blog posts
– Need to address high dimensionality and sparsity due to their
keyword-based approach
• Agarwal et al. 2008 proposed a collective-wisdom
based approach
– Generate a category relation graph based on user assignments
– Compute similarity matrix from this graph
Blog Mining
• Interactions between producers and consumers improved with blogs
• Consumers not only speak their mind but also broadcast their opinions
• Blogs are invaluable information sources
–
–
–
–
–
consumers’ beliefs and opinions,
initial reaction to a launch,
understand consumer language,
track trends and buzzwords, and
fine-tune information needs
• Blog conversations leave behind the trails of links, useful for
understanding how information flows and how opinions are shaped
and influenced
• Tracking blogs also help in gaining deeper insights
Blog Mining for Opinion
• A prototype system called Pulse [Gamon et al. 2005] uses a Naive Bayes
classifier trained on manually annotated sentences with positive/negative
sentiments and iterates until all unlabeled data is adequately classified.
• Another system presented in [Attardi and Simi 2006] improves the blog
retrieval by using opinionated words acquired from WordNet in the query
proximity.
• Some well-known opinion mining and sentiment analysis techniques [B.
Liu 2006] could also be borrowed from text mining domain due to high
textual nature of blogs.
• LingPipe (http://alias-i.com/lingpipe/demos/tutorial/sentiment/readme.html) is another open source software which performs sentiment
analysis on text corpora.
– Subjective (opinion) vs. Objective (fact) sentences
– Positive (favorable) vs. Negative (unfavorable) movie reviews
Influence
• Market Movers: “word-of-mouth”, trust and
reputation
• Sway opinions: Government policies, campaign
• Customer Support and Troubleshooting
• Market research surveys: “use-the-views”
• Representative articles: 18.6 new blog posts per sec
• Advertising
Blog Influence
• Two types of influence
– Influential blog sites and site networks [Gill 2004, Gruhl et al 2004, Java et al
2006]
– Influential bloggers in a community [Agarwal et al. 2008]
• Blogosphere vs. Friendship Networks
–
–
–
–
Implicit vs. Explicit links
Blog statistics vs. Centrality measures
“influencing” vs. “could influence”
Loosely vs. Strictly defined graph structures
• Blog vs. Webpage Ranking
– Blog sites too sparse for webpage ranking algorithms to work [Kritikopoulos et
al 2006]
– Webpage acquires authority over time, blog posts’ influence diminishes
– Greedy approach works better than PageRank, HITS to maximize influence
flow [Kempe et al 2003, Richardson & Domingos 2002]
Issue of Trust
• Open standards and low barriers to publishing have created
overwhelming amount of collective wisdom
• Yet more difficult for readers to discern whom to trust in
some cases
• Similar to WWW
– Authoritative webpages e.g., HITS [Kleinberg et al. 1998], PageRank
[Page et al. 1999]
• Blogosphere allow mass to create and edit content
compromising the sanctity of the original content
• Some work exists for social friendship network domain, not
many researchers have explored Blogosphere
• Huge potential for trust study in Blogosphere domain
Trust
• Kale et al. 2007 transformed the problem of trust in
blogosphere to the one in social friendship networks
– Studied propagation of trust among different blog sites
– Mined sentiments from a window of words around hyperlinks
– Identified positive, negative, or neutral sentiments towards the linked
blog site
– Constructed a network of blog sites using hyperlinks
– Used Gruhl et al. 2004 trust propagation algorithm
– Some concerns
• These blog sites have to be linked for trust propagation
• Trust is computed between blog sites based on how much one blog
agrees or disagrees with the other
Mi+1 = Mi * Ci – Perform till convergence
M = Belief Matrix; Ci = Atomic Propagation
Ci = M + MT*M + MT + M*MT
Community Extraction
• Blogosphere doesn’t have an explicit notion of communities
except community blogs
• Discovering communities among individual blogs based on
interaction
• Different from blog clustering
– Blog Clustering uses textual similarity
– Community extraction taps interaction
and link analysis
Community Extraction
• Blogosphere doesn’t have an explicit notion of communities
• Different from blog clustering
• Researchers identify communities based on
– Links: network of hyperlinks allows identification of virtual communities
• Several studies on finding community of webpages like Kleinberg 1998
and Kumar et al. 1999
• While Kleinberg used authority and hubs idea to explore communities of
webpages, Kumar et al. extended the idea of hubs and authorities and
included co-citations as a way to extract all communities on the web and
used graph theoretic algorithms to identify all instances of graph
structures that reflect community characteristics.
– Content: blogs with similar content or inspired by the same event form a
virtual community
• Kumar et al. 2003, Efimova and Hendrick 2005, Blanchard 2004
Community Extraction
• Chin and Chignell 2006 proposed a model for finding
communities taking the blogging behavior of bloggers into
account
– They aligned behavioral approaches through blog reader survey
in studying blog community.
• Blanchard and Marcus 2004 studied a multiple sport
newsgroup “Virtual Settlement” and analyzed the possibility
of emerging virtual communities
– Newsgroups and discussion forums are similar in terms of
interaction patterns to Blogosphere
– More person-to-group interaction rather than person-to-person
interaction
Spam blog (Splogs) Filtering
• One of the major rising concerns on Blogosphere
• Spammers make most of their money by getting viewers to click on ads that
run adjacent to their nonsensical text
• Open standards and low barriers to publishing escalates the problem and
challenges while solving
• Besides degrading search quality, affects the network resources
Spam blog (Splogs) Filtering
• One of the major rising concerns on Blogosphere
• Open standards and low barriers to publishing escalates the problem and
challenges while solving
• Besides degrading search quality, affects the network resources
• Initial researches applied web spam link detection approaches
– Ntoulas et al. 2006, distinguish between normal web pages and spam
webpages based on the statistical properties like
• number of words, average length of words, anchor text, title keyword frequency,
tokenized URL
– Gyongyi et al. 2004, Gyongyi et al. 2006 use PageRank to compute the spam
score of a webpage
• Kolari et al. 2006, consider each blog post as a static webpage and use
both content and hyperlinks to classify a blog post as spam using a SVM
based classifier
Spam blog (Splogs) Filtering
• Some critical differences between web spam detection and
splog detection
– The content on blog sites is very dynamic as compared to that of web pages,
so content based spam filters are ineffective
– Moreover, spammers can copy the content from some regular blog posts to
evade content based spam filters
– Link based spam filters can easily be beaten by creating legitimate links
• Lin et al. 2007, consider the temporal dynamics of blog posts
and propose a self similarity based splog detection algorithm
based on characteristic patterns found in splogs like,
– Regularities or patterns in posting times of splogs,
– Content similarity in splogs, and
– Similar links in splogs.
Opinion and Sentiment Analysis
• BLEWS (http://research.microsoft.com/projects/blews/blews.aspx)
– Using Blogs to Provide Context for News Articles
– Political views: Liberal vs. Conservative
– Emotional charge
Opinion and Sentiment Analysis
Opinion and Sentiment Analysis
• BLEWS (http://research.microsoft.com/projects/blews/blews.aspx)
– Using Blogs to Provide Context for News Articles
– Political views: Liberal vs. Conservative
– Emotional charge
• SKEWS (http://www.skewz.com/)
– Reveal bias in news story (articles)
– Users rate the story on a scale from Liberal to Conservative
– Readers vote
Opinion and Sentiment Analysis
Opinion and Sentiment Analysis
• BLEWS (http://research.microsoft.com/projects/blews/blews.aspx)
– Using Blogs to Provide Context for News Articles
– Political views: Liberal vs. Conservative
– Emotional charge
• SKEWS (http://www.skewz.com/)
– Reveal bias in news story (articles)
– Users rate the story on a scale from Liberal to Conservative
– Readers vote
• Opinion mining in legal blogs [Conrad and Schilder, 2007]
– Collected blogs on legal search tools
– N-gram Language modeling approach to determine
• Subjectivity of text
• Polarity of text
• Degree of polarity
TOOLS AND APIS
Analysis and Visualization Tools
• Tools
– Data Analysis & Visualization tools
– Statistics like centrality measures
• NetLogo (http://ccl.northwestern.edu/netlogo/)
– Multi-agent programming language and modeling environment
designed in Logo
– Modelers can give instructions to hundreds or thousands of
concurrently operating autonomous agents.
– Exploring the connection between the individuals (micro-level) and
the patterns that emerge from the interaction of many individuals
(macro-level).
Analysis and Visualization Tools
• StarLogo (http://education.mit.edu/starlogo/)
– An extension of Logo
– It is used to model the behavior of decentralized systems like social
networks.
• REPAST (http://repast.sourceforge.net/)
– Recursive Porous Agent Simulation Toolkit
– Agent-based social network modeling toolkit.
– It has libraries for genetic algorithms, neural networks, etc. and allows
users to dynamically access and modify agents at run time.
• Swarm (http://www.swarm.org/wiki/Main Page)
– A multi-agent simulation package
– Simulates social or biological interaction of agents and their emergent
collective behavior.
Analysis and Visualization Tools
• UCINet (http://www.analytictech.com/)
– Package for the analysis of social network data including centrality
measures, subgroup identification, role analysis, elementary graph
theory, and permutation-based statistical analysis
– Has strong matrix analysis routines, such as matrix algebra and
multivariate statistics
• Pajek (http://vlado.fmf.uni-lj.si/pub/networks/pajek/)
– Slovenian for spider
– Analyzing and visualizing large networks like social networks
• Network package in R (http://cran.r-project.org/src/contrib/Descriptions/network.htm)
– The network class can represent a range of relational data types, and
support arbitrary vertex/edge/graph attributes
– This is used to create and/or modify the network objects and is used
for social network analysis (SNA)
Analysis and Visualization Tools
• InFlow (http://www.orgnet.com/inflow3.html)
– Integrated product for network analysis and visualization
– Used in the SNA domain
• NetMiner (http://www.netminer.com/)
– Tool for exploratory network data analysis and visualization
– NetMiner allows to explore network data visually and
interactively, and helps in detecting underlying patterns
and structures of the network
APIs
• APIs
– Data collection (blog posts, inlinks, tags, etc.)
– Technorati
– Digg
– del.icio.us
– Facebook
– StumbleUpon
Technorati API
• bloginfo query
API url: http://api.technorati.com/bloginfo?key=[apikey]&url=[blog
Sample response:
<result>
<url>[URL]</url>
<weblog>
<name>[blog name]</name>
<url>[blog URL]</url>
<rssurl>[blog RSS URL]</rssurl>
<atomurl>[blog Atom URL]</atomurl>
<inboundblogs>[inbound blogs]</inboundblogs>
<inboundlinks>[inbound links]</inboundlinks>
<lastupdate>[date blog last updated]</lastupdate>
<rank>[blog ranking]</rank>
<lang></lang>
<foafurl>[blog foaf URL]</foafurl>
</weblog>
</result>
url]
Technorati API
• BlogPostTags query
API url: http://api.technorati.com/blogposttags?key=[apikey]&url=[blog
Sample response:
<document>
<result>
<querycount>[limit parameter]</querycount>
</result>
<item>
<tag>[tag name];/tag>
<posts>[tag count]</posts>
</item>
</document>
url]
Digg API
• List Stories
Api url:
http://services.digg.com/stories/popular?domain=engadget.com&count=10&mi
n_submit_date=[epoch(07/01/2008)]&max_submit_date=[epoch(07/15/1008)]&ap
pkey=[appkey]
Sample response:
Digg API
<story id="7511382" link="http://www.engadget.com/2008/07/15/dev-teamshows-off-video-of-worlds-first-jailbroken-iphone-3g/"
submit_date="1216139955" diggs="623" comments="38"
promote_date="1216186807" status="popular" media="news"
href="http://digg.com/apple/World_s_First_Jailbroken_iPhone_3G">
<title>World's First Jailbroken iPhone 3G</title>
<description>
We can't say this is a surprise... but it is sweet to
see. The iPhone Dev Team has added a video to their blog showing off the
lists 10
most popular
stories2.0,
from along with a video of
latest version of their
upcoming
PwnageTool
what they claim is the
"world's first" jailbroken iPhone 3G.
http://www.engadget.com
</description>
between 1st July 2008 and 15th July
<user name="jordankasteler"
2008
icon="http://digg.com/users/jordankasteler/l.png"
registered="1172914233"
profileviews="8344" fullname="Jordan Kasteler"/>
<topic name="Apple" short_name="apple"/>
<container name="Technology" short_name="technology"/>
<thumbnail originalwidth="500" originalheight="378"
contentType="image/jpeg"
src="http://digg.com/apple/World_s_First_Jailbroken_iPhone_3G/t.jpg"
width="80" height="80"/>
</story>
…
del.icio.us API
https://api.del.icio.us/v1/tags/get
Returns a list of tags and number of times used
Sample response
<tags>
<tag count="1"
<tag count="1"
<tag count="3"
<tag count="5"
<tag count="1"
<tag count="1"
</tags>
tag="activedesktop" />
tag="business" />
tag="radio" />
tag="xml" />
tag="xp" />
tag="xpi" />
DATA COLLECTION
Some Available Datasets
• Nielsen Buzzmetrics dataset (http://www.icwsm.org/format.txt)
– ~ 14M blog posts from 3M blog sites collected by Nielsen BuzzMetrics
in May 2006
– 1.7M blog-blog links
– Up to a half of the blog outlinks are missing
– 51% of the total blog posts are in English
• Enron Email dataset (http://www.cs.cmu.edu/~enron/)
– Emails from about 150 users
– The corpus contains a total of about 0.5M messages
– People have studied the social networks between users based on link
construction
– Links are constructed based on email senders and recipients
Available Datasets (2)
• TREC (http://ir.dcs.gla.ac.uk/test_collections/blog06info.html)
– A crawl of Feeds, and associated Permalink and homepage
documents (from late 2005 and early 2006)
– 100,649 feeds were polled once a week for 11 weeks
– Total Number of Feeds collected:753,681
– Average feeds collected every day:10,615
– Uncompressed Size:38.6GB Compressed Size:8.0GB
– Reasonably sized spam component for added realism
– Fee: £400 ~ $794.36
Available Datasets (3)
• Mobile Network (http://kdl.cs.umass.edu/data/msn/msn-info.html)
–
–
–
–
27 objects
over 180,000 links
1 object attribute
2 link attributes
• Other ways
–
–
–
–
Crawl blogs
Blogcatalog
Statistics available from technorati API
Tagging available from del.icio.us API
Data Crawler
• BlogTrackers
– User interface to crawl blog sites
• Scratch crawling (from blog archives)
• Incremental crawling (from RSS feeds)
– Stores the blog posts in Microsoft SQL server
– Collects
Blog post title
Blog post tags
Blog post content
Blog post permalink
Outlinks
Blogger name
Inlinks
Blog post date and time
– Track blog posts like generate tag clouds for user specified time
window
Collectable Statistics from Blogs
• Inbound links
– Blogs, blog post, webpage
• Outbound links
– Blogs, blog post, webpage
•
•
•
•
•
•
•
•
•
Comments
Blog server logs
Subscribers
Time to read/length
Links to post and incoming traffic from them
Links from post and outgoing traffic to them
Topic frequency score
Blogroll links
Tagged urls (del.icio.us, furl)
Citation Dataset
• DBLP (http://kdl.cs.umass.edu/data/dblp/dblp-info.html)
–
–
–
–
–
Over 1,200,000 objects
Over 2,480,000 links
12 object attributes
6 link attributes
910 MB
MEASURES, MODELS, AND
METHODS
Measures, Models, and Methods
• Centrality Measures
• Mathematical models: random, scale-free,
preferential attachment, hybrid, cascade
• Content analysis techniques
• Link analysis
• Supervised/unsupervised learning algorithms
• Decision theoretic approaches
• Agent-based modeling
Centrality Measures
• Degree centrality
– Defined as the number of ties a node has
Cd (v)  {e : M adj (v, v j )  0, j}
– For directed network
• Indegree ~ “popularity”
• Outdegree ~ “gregariousness”
– O(V2) for V vertices in dense network
– O(E) for E edges in sparse network
Centrality Measures
• Betweenness centrality
– a centrality measure of a vertex within a
graph
– Vertices that occur on many shortest
paths between other vertices have higher
betweenness than those that do not
– Act as “broker” or “bridge”
– O(V3) complexity
– O(V2logV+VE) for sparse network
 st (v)
C B (v )  
s  v  tV  st
s t
σst is the geodesic path between s and t. σst(v) is the geodesic path between s
and t passing through v.
Centrality Measures
• Closeness centrality
– A centrality measure of a vertex within a graph
– Vertices that tend to have short geodesic distances to
other vertices within the graph have higher closeness.
– Defined as the mean geodesic distance between a vertex v
and all other reachable vertices
d
tV \ v
G
(v, t )
n 1
– O(V3) complexity
Centrality Measures
• Eigenvector centrality
– Measure of the importance of a node in a network
– Assigns relative scores to all nodes in the network
– Better to connect to more “popular” nodes than
less “popular” ones
– Google's PageRank is a variant of the Eigenvector
centrality measure
xi 
1
N
A


j 1
i, j
xj
or
 1 
x  Ax

Mathematical Models
• Power law
– Polynomial relationship with scale invariance
f ( x)  ax 
– a and α are constants > 1
Power Law plot
Log-log plot of Power Law
Mathematical Models
• Power law
– Examples: fractals, inverse square law, Zipf law,
pareto rule, etc.
– Two aspects of real networks (e.g., Social
networks, Blog networks, World Wide Web,
biological networks, etc.) make power law models
an appropriate choice as compared to random
models
• Number of nodes (N) in the real networks is not static
• Most real networks exhibit preferential connectivity.
Mathematical Models
• Random
– Random network models assume the probability that two vertices are
connected is random and uniform P (e : v  v )   ,
0
• Preferential attachment
i
j
 1
– For example, a newly created webpage will be more likely to include links to
well-known documents with already high connectivity
– Thus the probability with which a new vertex connects to the existing vertices
is not uniform
P(e : vi  v j )  deg( vi ) / V
– This property of power law models is also known as preferential attachment
models
• Hybrid
– Pennock et al. 2002, have shown the relative importance of hybrid models in
simulating social networks
P(e : vi  v j )   deg( vi ) / V  (1   )
– Determine the appropriate proportion of random and scale free networks
Mathematical Models
• Cascade
– Model information diffusion across the network
– Linear threshold model
• Assumes a linear relation between influencing and influenced nodes
• Defines influencing capacity and tolerance limit of each node
• Sum of the influencing capacities of the neighboring nodes > tolerance
limit of this node, then this node gets influenced
– Independent cascade model
•
•
•
•
Assumes the process of influence flow as cascade of events
Event represents a node being influenced
Each node is assigned an influencing probability
If node v influences node w then at time t+1 w gets influenced. No more
attempts are made by v to influence w
• Algorithm terminates when it is not possible to influence anymore nodes
Content Analysis Techniques
• Blogs have rich textual content
• Not only people create new content, they also enrich
the existing content by providing meta data such as
labels and tags
• Human-generated tags are also called folksonomies
• State-of-the-art content analysis techniques could be
used for basic clustering, classification of the blog
posts/blog sites
Content Analysis Techniques
• tf-idf could be used for indexing the blog entries
• Folksonomies could be considered as class labels
• Supervised machine learning could be performed
and learned models could be used to predict the tags
of unlabeled corpus
• This forms an essential concept for semiautomatically generating tag-clouds with least
human intervention.
Link Analysis
• Directed graph representation of blogs
• Links form the edges of this graph
– Incoming links (inlinks)
– Outgoing links (outlinks)
• Link analysis helps in understanding several interesting
phenomena of social networks.
• Text around the links give us knowledge about the linked blog
posts.
• Based on the links, hubs and authorities could be discovered.
• This approach could lead to the identification of expert(s)
within communities.
• Link traversal: O(dh) for average outdegree d and h hops
Use of Link Analysis
• Sparsity in the link structure of social networks makes it
different from the World Wide Web model
• Many of them like Blogosphere assume implicit link
information among bloggers
• Links could be constructed using the topic analysis
• Blog posts talking about same topic could be connected
– Supervised learning algorithms could be used to predict topics of
unlabeled blogs
Decision Theoretic Approaches
• Group-individual interaction and the effect of decision on an
individual and/or a community as a whole.
• Decision theory studies what is the best possible decision to
take given a fully informed decision maker.
• In social networks find the node that is the best to make
decisions with least possible side-effects and maximum
possible gains for the rest of the nodes.
– Finding a node that has maximum information diffusion across
• The analysis of such social decisions is dealt through game
theory.
Agent-based Modeling
• Each node in a social network can be treated as an agent
[Sallach and Macal, 2001]
• This agent could be a blogger in the blogosphere
• Decision making ability of the agent can be modeled
probabilistically
• This can help us in studying the factors that affect his/her
blogging behavior, what and how (s)he makes decisions
• Neural networks or genetic algorithms could also be used
to train the model of these agents to closely simulate
real-world scenario [Axelrod and Tesfatsion, 2005]
PERFORMANCE, EVALUATION, AND
METRICS
Performance
• Does a project make any difference? We need to compare
– Previously proposed model(s)
– Baseline model(s)
• Basic criteria
– Efficiency (speed, scalability)
– Correctness (get what you aim to get)
• Traditional data mining/ machine learning performance criteria
–
–
–
–
–
Precision
Recall
F-measure
Area under ROC curve
Inter and intra cluster distances
• Often we assume some ground truth
• Training-testing models work on this assumption
Train
Test
Total number of examples
Evaluation Challenges in Blogosphere
• Concepts like influence, trust in Blogosphere can be
subjective and often change based on particular
needs
• No ground truth available
• Typical training-testing models may not work
• Often resort to human evaluation and surveys
– How to select subjects, and how many would suffice
– How big is the evaluation budget, how long is the duration
• Need to figure out objective ways of evaluation
Evaluation and Metrics
• Obviously, various tasks may require different
ways of performance evaluation
– Blog search and retrieval
– Clustering
– Classification
– Spam blogs
– Diffusion
– Influence
• We provide some illustrative examples next.
Blog Search and Retrieval
• Precision and Recall
– Typically evaluated on unordered sets of
documents
– Top k results generate k sets for different
values of k
– P and R evaluated at different top k
Recall
Interpolated Precision
0.0
1.00
0.1
0.67
0.2
0.63
0.3
0.55
0.4
0.45
0.5
0.41
0.6
0.36
0.7
0.29
0.8
0.13
0.9
0.10
1.0
0.08
• Interpolated Precision
– Defined as the highest precision at certain
recall
– Red line in the graph above shows the
interpolated precision
pip (r )  max p(r )
r  r
Blog Search and Retrieval
• Mean Average Precision (MAP)
– Average of the precision scores after each relevant
document retrieved for each query
– Mean of the individual average precision scores for all the
queries q є Q
1
MAP(Q) 
Q
Q
1

j 1 m j
mj
 P( R
k 1
jk
)
– Gives both precision and recall oriented aspects
– Generates a single value for the set of queries
– Less obvious interpretation than other measures
Measuring a Ranked List
• Normalized Discounted Cumulative Gain (NDCG)
•
•
•
•
•
Measuring relevance of returned search result
Multi levels of relevance (r): irrelevant (0), borderline (1), relevant (2)
Each relevant document contributes some gain to be cumulated
Gain from low ranked documents is discounted
Normalized by the maximum DCG
n
CG (d1 ,..., d n )   ri
i 1
n
DCG (d1 ,..., d n )  r1  
i 2
n
MaxDCG  R1  
i 2
ri
log 2 i
Ri
log 2 i
NDCG(d1 ,..., d n )  DCG (d1 ,..., d n ) / MaxDCG
NDCG - Example
4 documents: d1, d2, d3, d4
Ground Truth
Ranking Function1
Ranking Function2
i
Document
Order
ri
Document
Order
ri
Document
Order
ri
1
d4
2
d3
2
d3
2
2
d3
2
d4
2
d2
1
3
d2
1
d2
1
d4
2
4
d1
0
d1
0
d1
0
NDCGGT=1.00
NDCGRF1=1.00
 2
1
0 
  4.6309
DCGGT  2  


log
2
log
3
log
4
2
2
 2

 2
1
0 
  4.6309
DCGRF1  2  


log
2
log
3
log
4
2
2
 2

 1
2
0 
  4.2619
DCGRF 2  2  


 log 2 2 log 2 3 log 2 4 
MaxDCG  DCGGT  4.6309
NDCGRF2=0.9203
Comparing Two Ranked Lists
• Rank correlation
– Spearman’s rank correlation
coefficient
6 d i2
  1
n(n 2  1)
– Example
ρ = 1-(6*194/10*(102-1))
= -0.175
rank
xi
rank
yi
di
di2
1
0
0
2
6
-4
16
28
3
8
-5
25
100
27
4
7
-3
9
101
50
5
10
-5
25
103
29
6
9
-3
9
106
7
7
3
4
16
110
17
8
5
3
9
112
6
9
2
7
49
113
12
10
4
6
36
Xi
Yi
86
0
1
97
20
99
Concordance between a Pair
• Rank correlation
– [-1,1]: perfect agreement=1, perfect disagreement=-1
– Kendall tau rank correlation coefficient
– Example
4P

1
n(n  1)
Person
A
B
C
D
E
F
G
H
Rank by Height
1
2
3
4
5
6
7
8
Rank by Weight
3
4
1
2
5
7
8
6
P = 5 + 4 + 5 + 4 + 3 + 1 + 0 + 0 = 22
τ = (4*22/8*7 )-1= (88/56)-1 = 0.57
Blog Clustering
• Within cluster between cluster distance
– Small within cluster distance  Cohesive
– Large between cluster distance  well-separated clusters
•
•
•
•
Distance between cluster mean/centroids
Single linkage
Complete linkage
Average linkage
Cluster Mean/Centroids
Single Linkage
Cohesive, well-separated clusters
Complete Linkage
Average Linkage
Blog Clustering
• How many clusters should we have
– The elbow criterion can be used to pick the number of clusters
– Explained variance is ratio of between-group variance to total variance
Spam Blogs
AP
Train-Test model
Precision, Recall, F-measure based metrics
Precision (P) = TP/(TP+FP)
Where can we find FP, FN,
TP, and TN
Recall (R)= TP/(TP+FN)
F-measure (F) = 2*PR/(P+R)
Actual
Predicted
•
•
•
•
•
AN
spam
not-spam
spam
7
4
not-spam
3
6
TP=7, FP=4, FN=4, TN=6
P=7/11, R=7/10, F=0.663
CASE STUDIES
Case Studies
•
•
•
•
“Familiar Strangers” in Blogosphere
Employing Collective Wisdom
Blog Community Interaction
iFinder: Finding Influential Bloggers
“FAMILIAR STRANGERS”
Short Head and Long Tail
• Few people are densely
connected: Short Head
• Many people are sparsely
connected: Long Tail
• Businesses like Amazon,
Netflix, Wal-Mart, etc.
obey this phenomenon
• Wal-Mart sells more Long
Tail items than Short
Head
Short
Head
Long
Tail
• Zipf, Power Law, Pareto’s
Law generate Long Tail
Who are Familiar Strangers?
• Observe repeatedly, but do not know each other
• Real World
– E.g., Individuals observe each other daily on a train
– Discover the latent pattern: going to same workplace,
• Blogosphere
– What you write is what you are…
– Have similar blogging behavior, interests (Movie and
games, Technology, and Politics, etc.)
– Never cited (came across) each other
Bloggers in Long Tail
•
•
•
•
•
Not returned as top hits by search engines
Not popular
Inordinately many
Disconnected
Movie Critics – Short Head
(nytimes.com)
• Movie Bloggers – Long Tail
• Most lucrative test-bed for Familiar Strangers
Aggregating Niches in Long Tail
• A blogger’s familiar-strangers together form a
critical mass such that
– the understanding of one blogger gives us a sensible
and representative glimpse to others,
– more data about familiar strangers can be collected
for better customization and services (e.g.,
personalization and recommendation),
– the nuances among them present new business
opportunities, and
– knowledge about them can facilitate predictive
modeling and trend analysis.
Need for Aggregation
• Customized attention requires
substantial data
• Majority of blog sites are in the
Long Tail
• …and are disconnected
• Aggregating the similar yet
disconnected for obtaining critical
mass
• Lack of data can result in
irrelevant ads (see an example on
the right)
• Increase participation
• Move from the Long Tail closer to
the Short Head
• Smooth knowledge transfer
between familiar strangers
Definition – Familiar Strangers
• Given a blogger b, familiar strangers to b are a set of bloggers
B = {b1,b2,…,bn}, who share common patterns as b, like
blogging on similar topics, but have never come across each
other or have never related to each other.
• Familiar:
Blog posts
Definition – Familiar Strangers
• Strangers:
– Partial strangers
– Total strangers
• Partial strangers
bj is in b’s Social Network
b is in bj’s Social Network
Definition – Familiar Strangers
• Total strangers
b and bj have disjoint Social Networks
• We focus on total strangers
Types of Familiar Strangers
• Organizational differences in the blogosphere
eventuate disparate types of familiar stranger bloggers
Community-level
familiar strangers
Networking-site-level
familiar strangers
Blogosphere-level
familiar strangers
Community Level Familiar Stranger
• MySpace has a
community called “A
group for those who
love history”
• It has 38 members
• two members, “Maria”
and “John”
– blog profusely on the
similar topic,
– but they are not in each
other’s social network.
Networking Site Level Familiar Stranger
• 2 groups on MySpace,
– The Samurai (32 members)
– The Japanese Sword (84
members)
– Marc, top blogger on “The
Samurai” and Jeff, top
blogger on “The Japanese
Sword” discuss about
Japanese martial arts.
– Neither of them is in the
other’s social network.
– This implies, though being
active locally and discussing
on the same theme, the two
bloggers are still strangers.
Blogosphere Level Familiar Stranger
• 2 different social networking
sites, MySpace and Orkut.
– The Samurai (32 members)
from MySpace
– Samurai Sword (29 members)
from Orkut
– Top bloggers from the
respective communities in
MySpace and Orkut, Marc and
Anant, respectively, share the
blogging theme but they are
not in each others’ social
network.
– The above example illustrates
the existence of blogospherelevel familiar strangers.
Challenges
•
•
•
•
•
•
Link analysis
Defining Similarity
Data collection
Experiments
Evaluation & Validation
Current tools & technologies search the Short
Head
Search via Blog Posts
Search via Blogger’s Blog Post
Search via Context
Search via Blogger’s context
Leveraging User Contributions
iFinder
EMPLOYING COLLECTIVE WISDOM
What is Collective Wisdom?
• Shared knowledge arrived at by individuals
and groups, used to solve problems
• Group wisdom or Co-intelligence
• Blog Clustering
–
–
–
–
User generated content as well as user enriched content
A prominent feature of social web
Several users tag and categorize their blogs
Collective wisdom emerges
Why Collective Wisdom?
• Challenges with traditional approaches
–
–
–
–
–
High dimensionality
Sparsity
Do not leverage collective wisdom
Require number of clusters a priori
Similarity measure
Blog Categories
Blog level Tags
5 Most recent blog posts’ snippets
BlogCatalog
Blog Post level Tags
BlogCatalog taxonomy
WisClus clusters
Data Collection
Total Bloggers Crawled
• Blogcatalog, using 4 bloggers as seed, crawled their social
network in a breadth-first fashion
• Report number of unique bloggers recorded with different
number of seed bloggers (2,4,6)
14000
12000
10000
8000
6000
4000
2000
0
Total Number of Starting Bloggers
personal
0
celebrity
crafts
environment
science
pets
blog resources
travel
home & garden
religion
education &…
philosophy
food & drink
computers
shopping
sports
business
music
society
internet
health
writing
political
news & media
technology
humor
arts & ent
blogging
Number of Blog Sites
Dataset Characteristics
• Variations in the dataset – depending on the category taxonomy
– Top-level
– All-category
– One node-split: because of the skewed distribution of categories
12000
10000
8000
6000
4000
2000
Experiments & Results
• Link strength experiments: LinkStrength > 5
• Category taxonomy variations: All-category
• Baseline vs. WisClus
– K-means
– Hierarchical
Type
Baseline - BloggerSpace
WisClus - CategorySpace
WisClus - BloggerSpace
Method
Kmeans
Hierarchical
Kmeans
Hierarchical
Kmeans
Hierarchical
Within Avg
Between Avg
0.0363
0.2194
0.0890
0.3644
0.0615
0.2860
0.0857
0.2761
0.0844
0.7090
0.0849
0.8118
Visualization Results
Visualizations of clusters using Collective Wisdom
Visualization Results
Visualizations of clusters using Baseline approach
Visualization Results
Use Pajek to visualize the results
BLOG COMMUNITY INTERACTION
Blog Community Interaction Types
• Discover community interaction through links
http://www.tuaw.com/2007/
12/30/iphone-firmware-1-13-breaks-unlocks/
http://apple.slashdot.
org/article.pl?sid=06/
07/17/2046205
Interaction Through Observation
• Interaction through observed events
– Communities with similar sentiments could be aggregated
Like
Dislike
Dislike
Indifferent
-1
0
Macbook
Like
1
Proposed Approach – Flowchart
Identify an event
E.g., Saddam Hussein’s
Death Sentence
Compare these
Sentiments to observe
the interaction with
respect to an event
Analyze pre-event, duringevent, post-event blog posts
E.g., November-06,
December-06, January-07
Use “WeFeelFine” API
to filter the sentiments
Summarize the blog
posts to pick relevant
content
Generate Tag Clouds
A Running Example
J
2004
F M …. … D J
2005
F M … … D J
2006
F M … N D J
2007
F …. … D J
2008
F M … D
Saddam’s Verdict
Iraq the Model
Baghdad Burning
accept according agree
America
announced
Baghdad building cabinet decisions
defense dialogue first future have
increase looking mass partner
patriotic people plan political
powers regional see shares situation
solutions start state term will
army bad beginning channels
country dead demonstrations
down justice new occupation
outside right Saddam
Salahuddin security shut since
single some stupidity today
Zawra
Legend
Positive Sentiment
Negative Sentiment
http://videolectures.net/wsdm08_agarwal_iib/
IFINDER: IDENTIFYING INFLUENTIAL
BLOGGERS IN A COMMUNITY
Physical and Virtual World
Domain
Expert
Friends
Physical World
Online
Community
Virtual World
Introduction
• Inspired by the analogy between realworld and blog communities, we answer:
Who are the influentials in Blogosphere?
Can we find them?
?
Active Bloggers = Influential Bloggers
• Active bloggers may not be influential
• Influential bloggers may not be active
Searching The Influentials
• Active bloggers
– Easy to define
– Often listed at a blog site
– Are they necessarily influential
• How to define an influential blogger?
–
–
–
–
Influential bloggers have influential posts
Subjective
Collectable statistics
How to use these statistics
Intuitive Properties
• Social Gestures (statistics)
– Recognition: Citations (incoming links)
– An influential blog post is recognized by many. The more influential the
referring posts are, the more influential the referred post becomes.
– Activity Generation: Volume of discussion (comments)
– Amount of discussion initiated by a blog post can be measured by the
comments it receives. Large number of comments indicates that the blog
post affects many such that they care to write comments, hence
influential.
– Novelty: Referring to (outgoing links)
– Novel ideas exert more influence. Large number of outlinks suggests that
the blog post refers to several other blog posts, hence less novel.
– Eloquence: “goodness” of a blog post (length)
– An influential is often eloquent. Given the informal nature of
Blogosphere, there is no incentive for a blogger to write a lengthy piece
that bores the readers. Hence, a long post often suggests some necessity
of doing so.
• Influence Score = f(Social Gestures)
A Preliminary Model
• Additive models are good to determine the combined value of
each alternative [Fensterer, 2007]. It also supports preferential
independence of all the parameters involved in the final decision.
A weighted additive function can be used to evaluate trade-offs
between different objectives [Keeney and Raiffa, 1993].
| |
| |
m 1
n 1
InfluenceFlow( p)  win  I ( pm )  wout  I ( pn )
I ( p)  wcomm p  InfluenceFlow( p)
I ( p)  w( )  ( wcomm p  InfluenceFlow( p))
iIndex ( B)  max( I ( pl ))
Understanding the Influentials
• Are influential bloggers simply active bloggers?
• If not, in what ways are they different?
– Can the model differentiate them?
• Are there different types of influential bloggers?
• What other parameters can we include to evolve
the model?
• Are there temporal patterns of the influential
bloggers?
How to Evaluate the Model
• Where to find the ground truth?
– Lack of Training and Test data
– Any alternative?
• About the parameters
– How can they be determined
– Are they all necessary?
• Are any of these correlated?
• Data collection
– A real-world blog site
– “The Unofficial Apple Weblog”
Active & Influential Bloggers
•
•
•
Active and Influential Bloggers
Inactive but Influential Bloggers
Active but Non-influential Bloggers
•
We don’t consider “Inactive and Non-influential Bloggers”, because they
seldom submit blog posts. Moreover, they do not influence others.
Lesion Study
• To observe if any parameter is irrelevant.
Other Parameters
• Rate of Comments
“Spiky” comments reaction
“Flat” comments reaction
Temporal Patterns of Influential
Bloggers
• Long term Influentials
• Average term Influentials
• Transient Influentials
• Burgeoning Influentials
Verification of the Model
• Revisit the challenges
– No training and testing data
– Absence of ground truth
– Subjectivity
• We use another Web 2.0 website, Digg as a reference
point.
• “Digg is all about user powered content. Everything is
submitted and voted on by the Digg community. Share,
discover, bookmark, and promote stuff that‘s important
to you!”
• The higher the digg score for a blog post is, the more it is
liked.
• A not-liked blog post will not be submitted thus will not
appear in Digg.
Verification of the Model
• Digg records top 100 blog posts.
• Top 5 influential and top 5 active bloggers were picked to construct 4
categories
• For each of the 4 categories of bloggers, we collect top 20 blog posts from
our model and compare them with Digg top 100.
• Distribution of Digg top 100 and TUAW’s 535 blog posts
Verification of the Model
• Observe how much our model aligns with Digg.
• Compare top 20 blog posts from our model and Digg.
• Considered last six months
• Considered all configuration to study relative importance of each parameter.
• Inlinks > Comments > Outlinks > Blog post length
Some Call for Papers
• ACM TKDD Special Issue on Social Computing
http://www.public.asu.edu/~huanliu/acm-tkdd-sbp
• Second International Conference on Social
Computing, Behavioral Modeling, and
Prediction (SBP09)
http://www.public.asu.edu/~huanliu/sbp09
• SIAM International Conf on Data Mining (SDM)
Sparks (Reno area), Nevada, April 30 - May 2, 2009.
http://www.siam.org/meetings/sdm09
References
[Adar and Adamic, 2005] Adar, E. and Adamic, L. A. (2005). Tracking information epidemics in blogspace. In WI ’05: Proceedings of
the The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI’05), pages 207–214, Washington, DC, USA.
IEEE Computer Society.
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