The document discusses research issues related to analyzing the blogosphere. It provides background on web 2.0, social networks, and defines blogs and the blogosphere. It then discusses key research issues such as understanding the structures and relationships within the blogosphere, modeling and clustering blogs, identifying influential blogs, issues of trust, extracting blog communities, and filtering spam blogs. Tools and APIs for collecting and analyzing blog data are also mentioned.
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Blogosphere by FrancoSH
1. Blogosphere: Research Issues, Tools
xxxx
and Applications
Franco Sánchez Huertas
(UCSP)
EDA – June, 2010
21/06/2010 UCSP -FASH 1
2. Overview
• Background: Web 2.0 and Social Networks
• Blogosphere: Definition, Types, and Comparison
• Blogosphere Research Issues
• Tools and APIs
• Data Collection
• Searching the Influentials: The Top Bloggers
• Conclusions
21/06/2010 UCSP -FASH 2
3. Web 2.0 and Social Networks
21/06/2010 UCSP -FASH 3
4. Caracteristics 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
21/06/2010 UCSP -FASH 4
5. Technology Overview of Web 2.0
• Cascading Style Sheets to aid in the separation of presentation and
content
• Folksonomies (collaborative tagging, social classification, social
indexing, and social tagging)
• REST and/or XML- and/or JSON-based APIs
• Rich Internet application techniques, often Ajax and/or Flex, Flash-
based
• Semantically valid XHTML and HTML markup
• Syndication, aggregation and notification of data in RSS or Atom
feeds
• mashups, merging content from different sources, client- and
server-side
• Weblog-publishing tools
• wiki or forum software to support user-generated content
21/06/2010 UCSP -FASH 5
6. Some Web 2.0 Services
• Blogs
– Blogspot
– Wordpress
– Lamula (Perú)
• Wikis
– Wikipedia
– Wikiversity
• Social Networking Sites
– Facebook
– Twitter
– MySpace
– Orkut
• Digital media sharing websites
– Youtube
– Flickr
– Vimeo
– Twitpic
• Social Tagging
– Del.icio.us
21/06/2010 UCSP -FASH 6
7. 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
21/06/2010 UCSP -FASH 7
9. 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
21/06/2010 UCSP -FASH 9
10. BLOGOSPHERE
Definitions, Types, and Comparison
21/06/2010 UCSP -FASH 10
11. 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
21/06/2010 UCSP -FASH 11
15. 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 a group of like-minded
Owned and maintained by individual users.
users.
More like discussion forums and discussion
More like personal accounts, journals or diaries.
boards.
High degree of group discussion and
No or almost negligible group interaction.
collaboration.
Enormous collective wisdom and open source
No or almost negligible collective wisdom.
intelligence.
• Regulated vs. anonymous
21/06/2010 UCSP -FASH 15
16. Blogosphere
• Complex Social Networks
• Vertices (Nodes): Bloggers/
Blog posts/Blog sites
• Edges: Relationships/Links
• In-Degree: Number of
inlinks
• Out-Degree: Number of
outlinks
21/06/2010 UCSP -FASH 16
17. 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
21/06/2010 UCSP -FASH 17
18. Friendship Networks vs. Blogosphere
Social Networks
Orkut, Facebook, LinkedIn,
Classmates.com, etc.
Social LiveJournal, MySpace, etc.
Friendship Blogosphere
Networks TUAW, Blogger, Windows Live
Spaces, etc.
21/06/2010 UCSP -FASH 18
20. Understanding Blogosphere
• Blogosphere • Everyone can publish, but
• Blog sites few are heard
• Bloggers • Many interesting questions
• Blog posts to address
• Reverse chronologically – How to build traffic
ordered entries – How to find niche
• Blogroll online
• Permalinks – How to increase
influence
– How to …
• Fertile research domain
21/06/2010 UCSP -FASH 20
21. 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.
21/06/2010 UCSP -FASH 21
22. 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
21/06/2010 UCSP -FASH 22
23. 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
21/06/2010 UCSP -FASH 23
24. 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
21/06/2010 UCSP -FASH 24
25. 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
21/06/2010 UCSP -FASH 25
27. 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
21/06/2010 UCSP -FASH 27
28. tfidf i , j tf i , j idf i
Blog Clustering
ni , j
tf i , j
• Brooks and Montanez 2006, used tf-idf and k
nk , j
picked top 3 keywords for blog posts log
D
d : ti d j
idf
– Clustered blogs based on these keywords
i
j
– Reported improved clustering as compared to that using tags
• 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
21/06/2010 UCSP -FASH 28
29. 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
21/06/2010 UCSP -FASH 29
30. 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]
21/06/2010 UCSP -FASH 30
31. 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
21/06/2010 UCSP -FASH 31
32. 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
21/06/2010 UCSP -FASH 32
33. 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
21/06/2010 UCSP -FASH 33
34. 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
21/06/2010 UCSP -FASH 34
35. 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
21/06/2010 UCSP -FASH 35
36. 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
21/06/2010 UCSP -FASH 36
37. Tools and API’s
Working in the Blogosphere…
21/06/2010 UCSP -FASH 37
38. 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).
21/06/2010 UCSP -FASH 38
39. 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)
21/06/2010 UCSP -FASH 39
40. 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
21/06/2010 UCSP -FASH 40
44. 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="activedesktop" />
<tag count="1" tag="business" />
<tag count="3" tag="radio" />
<tag count="5" tag="xml" />
<tag count="1" tag="xp" />
<tag count="1" tag="xpi" />
</tags>
21/06/2010 UCSP -FASH 44
45. Data Collection
Using the Blogosphere…
21/06/2010 UCSP -FASH 45
46. Available Datasets
• 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
21/06/2010 UCSP -FASH 46
47. Available Datasets
• 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
21/06/2010 UCSP -FASH 47
48. 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
21/06/2010 UCSP -FASH 48
49. 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)
21/06/2010 UCSP -FASH 49
50. Searching The Influentials : The Top Bloggers
• 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
21/06/2010 UCSP -FASH 50
51. 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)
21/06/2010 UCSP -FASH 51
52. 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?
21/06/2010 UCSP -FASH 52
53. Active & Influential Bloggers
• Active and Influential Bloggers
• Inactive but Influential Bloggers
• Active but Non-influential Bloggers
• They don’t consider “Inactive and Non-influential Bloggers”, because they
seldom submit blog posts. Moreover, they do not influence others.
21/06/2010 UCSP -FASH 53
54. Conclusions…
Blogosphere is one of the fastest growing, social networking
media. The virtual communities in the blogosphere are not
constrained by physical proximity and allow anytime, anywhere,
and instant communications.
In this paper the autors discuss current research issues in
Blogosphere including modeling, blog clustering, blog mining,
community discovery and factorization, influence and
propagation, trust and reputation, and filtering spam blogs.
21/06/2010 UCSP -FASH 54
