Blogosphere: Research Issues, Tools and Applications

Blogosphere: Research Issues, Tools and Applications
Huan Liu and Nitin Agarwal {Huan.Liu, Computer Science and Engineering Arizona State University Welcome the audience Introduce Nitin and us The updated version is available at our URL’s Let’s do a quick poll: How many visited a blog recently? How many have a blog? An updated version could be downloaded from or

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 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 ) us. The ppt version contains notes with additional information such as various sources in addition to References.

Outline 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 We will organize the tutorial roughly into 5 30-minutes mini-sessions: approximately two bullet points a mini-session. Two mini-sessions before the coffee break and three after the break. There are approximately 160 pages including references. We may not be able to cover all slides. We hope to provide the basics in this tutorial for you to explore further Case studies also demonstrate how to put some of the discussed components together and solve some interesting problems.

WEB 2.0 AND SOCIAL NETWORKS
The conducive environment that ushers Blogging into a new era Because of Web 2.0, there is a surge of interest in online communities and naturally social networks We therefore discuss the two topics briefly WEB 2.0 AND SOCIAL NETWORKS

Web vs. Web 2.0 http://en.wikipedia.org/wiki/Web_2.0
Coined by Tim O’Reily The former consumers are now producers

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 What makes web 2.0 unique RSS Really Simple Syndication

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

Web 2.0 Services (examples)
Blogs Blogspot Wordpress Wikis Wikipedia Wikiversity Social Networking Sites Facebook Myspace Orkut Digital media sharing websites Youtube Flickr Social Tagging Del.icio.us Others Twitter Yelp WordPress – Express yourself Twitter is a service for friends, family, and co–workers to communicate and stay connected through the exchange of quick, frequent answers to one simple question: What are you doing? Yelp is the fun and easy way to find, review and talk about what’s great

Top 20 Most Visited Websites
Internet traffic report by Alexa on July 29th 2008 40% of the top 20 websites are Web 2.0 sites 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

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 More and more people flock to the Web using various applications for social purposes – To gain fame To offer help, suggestions, opinions, … To make friends or re-discover your long lost friends In many ways, Web 2.0 is expanding our social networks from physical to virtual

Social Networks What social networks look like
Some are densely connected, some are sparsely connected, and some are small and

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 Folksonomy (also known as collaborative tagging, social classification, social indexing, and social tagging) is the practice and method of collaboratively creating and managing tags to annotate and categorize content.

Some Related CFPs ACM TKDD Special Issue on Social Computing
A Little Detour ACM TKDD Special Issue on Social Computing Second International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP09) SIAM International Conf on Data Mining (SDM) Sparks (Reno area), Nevada, April 30 - May 2, 2009.

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 Now let’s study why blogosphere is interesting Blogosphere – The New Political Arena by Michaek Keren 2006

“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 tech-oriented blogs on its list, …, were sold earlier this year, reportedly for sums in excess of $25 million.” The two examples show the new trend of advertising and the values of good blogs 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 This shows the number of blogs tracked by technorati 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
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 A blogroll is a listing of websites that often appear as links on weblogs. This list of links is used to relate the site owner's interest in or affiliation with other webloggers. Permalink Permanent link. The unique URL of a single post. Use this when you want to link to a post somewhere. TrackBack A system that allows a blogger to see who has seen the original post and has written another entry concerning it. The system works by sending a 'ping' between the blogs, and therefore providing the alert.

Blog Site

Blog Post

Blogger

Types of Blogs Individual vs. community Regulated vs. anonymous
Single authored (Individual blog sites) Multi authored (Community blog sites) Regulated vs. anonymous 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.

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
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 connections and/or location in the network Member’s Reputation/Trust based on the response to other member’s knowledge solicitations How Blogosphere differs from friendship networks How many of you have a facebook or mySpace account, or even better used it?

Friendship Networks vs. Blogosphere
Social Networks Orkut, Facebook, LinkedIn, Classmates.com, etc. LiveJournal, MySpace, etc. TUAW, Blogger, Windows Live Spaces, etc. Social Friendship Networks Blogosphere

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) How Blogosphere differs citation networks Researchers did use citation networks as a test-bed to study some aspects of Blogsphere, By now we have looked at the big environment in which Blogging is flourishing, why it will continue to be so, what are key components of the blogosphere We are ready to investigate further

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. Now lets look at the current research issues in the blogosphere along with some of the representative works.

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 However, not many blogosphere specific models have been developed. So here we review some of the models developed for www and how other works have modified or extended these to suit blogosphere. Often associated with modeling the web.

Modeling Blogosphere Preferential attachment
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 SIS: susceptible-infected-susceptible

Modeling Blogosphere Preferential attachment Hybrid model
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 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 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 use blogrolls on the blog posts to construct a network of blog posts assuming that blogrolls contain similar blog posts SIS: susceptible-infected-susceptible

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) Brooks and Montanez 2006, used tf-idf and
picked top 3 keywords for blog posts Clustered blogs based on these keywords Reported improved clustering as compared to that using tags Li et al 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 proposed a collective-wisdom based approach Generate a category relation graph based on user assignments Compute similarity matrix from this graph i-th term in the j-th doc

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 ( 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 Next we move onto the influence in blogosphere. Lets look at some applications of determining influence in the blogosphere. There is a lot of word-of-mouth advertising in online media specially blogosphere. people talk, listen and follow. In such a scenario, influential bloggers could act as market movers. Advertising-identify products which the influential bloggers give positive feedback for and place ads of these products. there is a high chance that readers after reading the informative piece by influential blogger click on the ad for this product.

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] webpage ranking approaches cannot be used to find influential bloggers due to some significant differences pointed out by existing research works.

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 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 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 is strictly based on interaction patterns either discovered through link analysis or content analysis w.r.t. a particular event

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

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 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

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 ( Using Blogs to Provide Context for News Articles Political views: Liberal vs. Conservative Emotional charge

BLEWS ( Using Blogs to Provide Context for News Articles Political views: Liberal vs. Conservative Emotional charge SKEWS ( Reveal bias in news story (articles) Users rate the story on a scale from Liberal to Conservative Readers vote

BLEWS ( Using Blogs to Provide Context for News Articles Political views: Liberal vs. Conservative Emotional charge SKEWS ( 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
Data Analysis & Visualization tools Statistics like centrality measures 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).

StarLogo ( An extension of Logo It is used to model the behavior of decentralized systems like social networks. REPAST ( 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 ( Page) A multi-agent simulation package Simulates social or biological interaction of agents and their emergent collective behavior.

UCINet ( 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 ( Slovenian for spider Analyzing and visualizing large networks like social networks Network package in R ( 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)

InFlow ( Integrated product for network analysis and visualization Used in the SNA domain NetMiner ( 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 various social networking sites provide APIs nowadays. this helps the developers to get limited access to data. APIs are also used to write numerous applications that extend the functioanlities of these sites and create mashups.

Technorati API bloginfo query
API url: url] 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>

Technorati API BlogPostTags query
API url: url] Sample response: <document> <result> <querycount>[limit parameter]</querycount> </result> <item> <tag>[tag name];/tag> <posts>[tag count]</posts> </item> </document>

Digg API List Stories Api url: Sample response:

Digg API <story id=" " link=" submit_date=" " diggs="623" comments="38" promote_date=" " status="popular" media="news" href=" <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 latest version of their upcoming PwnageTool 2.0, along with a video of what they claim is the "world's first" jailbroken iPhone 3G. </description> <user name="jordankasteler" icon=" registered=" " 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=" width="80" height="80"/> </story> … lists 10 most popular stories from between 1st July 2008 and 15th July 2008

del.icio.us API Returns a list of tags and number of times used
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>

data collection is an essential and critical component in any research
data collection is an essential and critical component in any research. now we discuss some of the datasets available for blogosphere related research works and ways to crawl more. DATA COLLECTION

Some Available Datasets
Nielsen Buzzmetrics dataset ( ~ 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 dataset ( s 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 senders and recipients

Available Datasets (2) TREC ( 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 ( 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 Track blog posts like generate tag clouds for user specified time window Blog post title Blog post tags Blog post content Blog post permalink Outlinks Blogger name Inlinks Blog post date and time

Collectable Statistics from Blogs
Inbound links Blogs, blog post, webpage Outbound links 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 ( Over 1,200,000 objects Over 2,480,000 links 12 object attributes 6 link attributes 910 MB

MEASURES, MODELS, AND METHODS
Now lets move to various measures, models and methods that form the baseline for research in this domain 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 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 σst is the geodesic path between s and t. σst(v) is the geodesic path between s and t passing through v. 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 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 or

Mathematical Models Power law
Polynomial relationship with scale invariance a and α are constants > 1 Lets look at some mathematical models that are often associated with blogosphere for instance, edge distribution, link distribution, etc. 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 Preferential attachment Hybrid
Random network models assume the probability that two vertices are connected is random and uniform Preferential attachment 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 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 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 semi-automatically 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
A critical part of Blogosphere research to ensure we make steady progress in research and development 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 What is a typical procedure of evaluation in data mining? 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 Examples for trust: buying a laptop battery vs. getting some 2nd opinion of some life threatening disease No ground truth available – Blogosphere is vast, changing, and sometime elusive. Using human subjects may be not as easy as it sounds

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. A concrete example will be shown later in Case Studies

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

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 Gives both precision and recall oriented aspects Generates a single value for the set of queries Less obvious interpretation than other measures j-th query, m_j documents, P – precision, R_j,k is k-th relevant document for j-th query

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 Why NDCG? Sensitive to the position of highest rated page Log-discounting of results Normalized for different lengths lists

NDCG - Example 4 documents: d1, d2, d3, d4 i Ground Truth
Ranking Function1 Ranking Function2 Document Order ri 1 d4 2 d3 d2 3 4 d1 NDCGGT=1.00 NDCGRF1=1.00 NDCGRF2=0.9203

Comparing Two Ranked Lists
Rank correlation Spearman’s rank correlation coefficient Example ρ = 1-(6*194/10*(102-1)) = Xi Yi rank xi yi di di2 86 1 97 20 2 6 -4 16 99 28 3 8 -5 25 100 27 4 7 -3 9 101 50 5 10 103 29 106 110 17 112 49 113 12 36 d is the difference or (x –y)

Concordance between a Pair
Rank correlation [-1,1]: perfect agreement=1, perfect disagreement=-1 Kendall tau rank correlation coefficient Example Person A B C D E F G H Rank by Height 1 2 3 4 5 6 7 8 Rank by Weight A term in P is the number of items that remain in agreement in the two comparing lists P = = 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 Cohesive, well-separated clusters Cluster Mean/Centroids Single Linkage 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 Train-Test model Precision, Recall, F-measure based metrics
AN AP Train-Test model Precision, Recall, F-measure based metrics Precision (P) = TP/(TP+FP) Recall (R)= TP/(TP+FN) F-measure (F) = 2*PR/(P+R) Where can we find FP, FN, TP, and TN Actual spam not-spam 7 4 3 6 Predicted 0.882/1.33 Actual negative (AN), actual positive (AP) Now we are ready to use we have discussed so far to examine some interesting problems. 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 Long Tail Zipf, Power Law, Pareto’s Law generate Long Tail Chris Anderson’s “The Long Tail” Albert-Laszlo Barabasi’s Linked

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 Irrelevant ads, too frequent appearances of them can discourage readers to even browse any ads

Web 2.0 and 4P Personalization Participation Peer-to-peer
Predictive modeling

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

Partial strangers Total strangers bj is in b’s Social Network b is in bj’s Social Network

Total strangers We focus on total strangers b and bj have disjoint Social Networks

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 blogosphere-level 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
However, how to reach those that cannot be reached by any search engines remains unsolved Search via Blogger’s context

Leveraging User Contributions
One possible solution of using user generated contents

EMPLOYING COLLECTIVE WISDOM
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

BlogCatalog Blog Categories Blog level Tags Blog Post level Tags
5 Most recent blog posts’ snippets BlogCatalog

BlogCatalog taxonomy WisClus clusters

Data Collection 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)

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

Experiments & Results Link strength experiments: LinkStrength > 5
Category taxonomy variations: All-category Baseline vs. WisClus K-means Hierarchical Type Method Within Avg Between Avg Baseline - BloggerSpace Kmeans 0.0363 0.2194 Hierarchical 0.0890 0.3644 WisClus - CategorySpace 0.0615 0.2860 0.0857 0.2761 WisClus - BloggerSpace 0.0844 0.7090 0.0849 0.8118

Visualization Results
Visualizations of clusters using Collective Wisdom

Visualizations of clusters using Baseline approach

Use Pajek to visualize the results

BLOG COMMUNITY INTERACTION

Blog Community Interaction Types
Discover community interaction through links

Interaction Through Observation
Interaction through observed events Communities with similar sentiments could be aggregated Macbook Dislike Like -1 1 Dislike Indifferent Like

Proposed Approach – Flowchart
E.g., Saddam Hussein’s Death Sentence Identify an event Analyze pre-event, during-event, post-event blog posts E.g., November-06, December-06, January-07 Summarize the blog posts to pick relevant content Generate Tag Clouds Use “WeFeelFine” API to filter the sentiments Compare these Sentiments to observe the interaction with respect to an event

A Running Example 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 Iraq the Model Baghdad Burning Saddam’s Verdict

IFINDER: IDENTIFYING INFLUENTIAL BLOGGERS IN A COMMUNITY
IFINDER: IDENTIFYING INFLUENTIAL BLOGGERS IN A COMMUNITY

Physical and Virtual World
Domain Expert Friends Online Community How often do you consult with your friends and relatives? Physical World Virtual World

Who are the influentials in Blogosphere?
Introduction Inspired by the analogy between real-world and blog communities, we answer: Who are the influentials in Blogosphere? Can we find them? ? Active Bloggers = Influential Bloggers Simply posting more blog posts doesn’t imply that the blogger is influential A voluble person may not be influential 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) In experiments we observe outlinks is negatively correlated with the number of comments received on a blog post, which means more outlinks reduces people's interest/attention. In experiments we observe blog post length is positively correlated with the number of comments received on a blog post, which means longer blog posts attracts people's interest/attention.

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]. I – information score which is normalized between 0 and 1, p – a blog post Other models are possible

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? Can we tune the weights in the model to identify different types of 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 So far we have tried to understand the influential bloggers, how they are different from active bloggers, different types of influential bloggers and their temporal patterns, and model parameters. But still we haven’t answered an important question, i.e. how do we evaluate the model. Specially when there is no ground truth or training data. One obvious solution could be to do human evaluation but it involves a lot of cost, effort and resources. Even then we cannot guarantee the fairness of the evaluation.

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. Consider digg as large online user survey. Spend more time in highlighting the problem. Screenshot of digg 148

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 S1: 71/327 = .217 S2: 14/42=.33 S3: 8/131 = .061 S4: 7/35 = .2 535 blog posts were submitted on TUAW in Jan 2007 from 14 bloggers (3, 2, 2, 7) 149

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
Second International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP09) SIAM International Conf on Data Mining (SDM) Sparks (Reno area), Nevada, April 30 - May 2, 2009.

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. [Adar et al., 2004] Adar, E., Zhang, L., Adamic, L., and Lukose, R. (2004). Implicit structure and the dynamics of blogspace. In Proceedings of the 13th International World Wide Web Conference. [Agarwal et al., 2007a] Agarwal, N., Galan, M., and Chen, Y. (2007a). Approximate structural matching over ordered xml documents. In International Database Engineering and Applicaion Symposium. [Agarwal et al., 2008a] Agarwal, N., Galan, M., Liu, H., and Subramanya, S. (2008a). Clustering blogs with collective wisdom. In Proceedings of the International Conference on Web Engineering (ICWE08). [Agarwal et al., 2005] Agarwal, N., Haque, E., Liu, H., and Parsons, L. (2005). Research paper recommender system: A subspace clustering approach. In The 6th International Conference on Web-Age Information Management (WAIM 2005), pages 475 – 491. [Agarwal et al., 2006a] Agarwal, N., Haque, E., Liu, H., and Parsons, L. (2006a). A subspace clustering framework for research group collaboration. International Journal of Information Technology and Web Engineering, 1(1):35 – 58. [Agarwal and Liu, 2008a] Agarwal, N. and Liu, H. (2008a). Blogosphere: Research issues, tools, and applications. SIGKDD Explorations. [Agarwal and Liu, 2008b] Agarwal, N. and Liu, H. (2008b). A study of communities and inﬂuence in blogosphere. In 2nd SIGMOD PhD Innovative Database and Research Doctorate Consortium (IDAR08), Vancouver, Canada. [Agarwal et al., 2008b] Agarwal, N., Liu, H., Salerno, J. J., and Sundarajan, S. (2008b). Understanding group interaction in blogosphere: A case study. In 2nd International Conference on Computational Cultural Dynamics (ICCCD08), Washington D.C. [Agarwal et al., 2007b] Agarwal, N., Liu, H., Salerno, J. J., and Yu, P. S. (2007b). Searching for Familiar Strangers on Blogosphere: Problems and Challenges. In NSF Symposium on Next-Generation Data Mining and Cyber-enabled Discovery and Innovation (NGDM).

References [Agarwal et al., 2008c] Agarwal, N., Liu, H., Tang, L., and Yu, P. S. (2008c). Identifying the influential bloggers. In Proccedings of the First ACM International Conference on Web Search and Data Mining (WSDM08) (Video available at: agarwal iib/). [Agarwal et al., 2006b] Agarwal, N., Liu, H., and Zhang, J. (2006b). Blocking objectionable web content by leveraging multiple information sources. SIGKDD Explor. Newsl., 8(1):17–26. [Albert, 2001] Albert, R. (2001). Statistical mechanics of complex networks. PhD thesis. [Aleman-Meza et al., 2006] Aleman-Meza, B., Nagarajan, M., Ramakrishnan, C., Ding, L., Kolari, P., Sheth, A. P., Arpinar, I. B., Joshi, A., and Finin, T. (2006). Semantic analytics on social networks: experiences in addressing the problem of conflict of interest detection. In WWW ’06: Proceedings of the 15th international conference on World Wide Web, pages 407–416, New York, NY, USA. ACM Press. [Ali-Hasan and Adamic, 2007] Ali-Hasan, N. and Adamic, L. (2007). Expressing social relationships on the blog through links and comments. In International Conference on Weblogs and Social Media. [Anderson, 2006] Anderson, C. (2006). The long tail : why the future of business is selling less of more. New York : Hyperion. [Attardi and Simi, 2006] Attardi, G. and Simi, M. (2006). Blog mining through opinionated words. In Proceedings of the fifteenth Text REtrieval Conference (TREC). [Avesani et al., 2005] Avesani, P., Massa, P., and Tiella, R. (2005). A trust-enhanced recommender system application: Moleskiing. In SAC, pages 1589–1593. [Backstrom et al., 2006] Backstrom, L., Huttenlocher, D., Kleinberg, J., and Lan, X. (2006). Group formation in large social networks: membership, growth, and evolution. In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 44–54, New York, NY, USA. ACM Press. [Barabasi and Albert, 1999] Barabasi, A. L. and Albert, R. (1999). Emergence of scaling in random networks. Science, 286(509). [Bekkerman and McCallum, 2005] Bekkerman, R. and McCallum, A. (2005). Disambiguating web appearances of people in a social network. In WWW ’05: Proceedings of the 14th international conference on World Wide Web, pages 463–470, New York, NY, USA. ACM Press.

References [Blanchard and Markus, 2004] Blanchard, A. and Markus, M. (2004). The experienced sense of a virtual community: Characteristics and processes. The DATA BASE for Advances in Information Systems, 35(1). [Blum et al., 2006] Blum, A., Mugizi, T. H. C., and Rwebangira, M. R. (2006). A random-surfer web-graph model. In Third Workshop on Analytic Algorithmics and Combinatorics (ANALCO06). [Bonhard et al., 2006] Bonhard, P., Harries, C., McCarthy, J., and Sasse, M. A. (2006). Accounting for taste: using proﬁle similarity to improve recommender systems. In CHI ’06: Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 1057–1066, New York, NY, USA. ACM Press. [Brin and Page, 1998] Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7):107–117. [Brooks and Montanez, 2006] Brooks, C. H. and Montanez, N. (2006). Improved annotation of the blogosphere via autotagging and hierarchical clustering. In WWW ’06: Proceedings of the 15th international conference on World Wide Web, pages 625–632, New York, NY, USA. ACM Press. [Cai et al., 2005] Cai, D., Shao, Z., He, X., Yan, X., and Han, J. (2005). Mining hidden community in heterogeneous social networks. In LinkKDD ’05: Proceedings of the 3rd international workshop on Link discovery, pages 58–65, New York, NY, USA. ACM Press. [Cai-Nicolas Ziegler, 2004] Cai-Nicolas Ziegler, G. L. (2004). Analyzing correlation between trust and user similarity in online communities. In iTrust, number [Chi et al., 2006] Chi, Y., Tseng, B. L., and Tatemura, J. (2006). Eigen-trend: trend analysis in the blogosphere based on singular value decompositions. In CIKM ’06: Proceedings of the 15th ACM international conference on Information and knowledge management, pages 68–77, New York, NY, USA. ACM Press. [Chin and Chignell, 2006] Chin, A. and Chignell, M. (2006). A social hypertext model for ﬁnding community in blogs. In HYPERTEXT ’06: Proceedings of the seventeenth conference on Hypertext and hypermedia, pages 11–22, New York, NY, USA. ACM Press.

References [Coffman and Marcus, 2004] Coffman, T. and Marcus, S. (2004). Dynamic classification of groups through social network analysis and hmms. In Proceedings of IEEE Aerospace Conference. [Conrad and Schilder, 2007] Conrad, J. G. and Schilder, F. (2007). Opinion mining in legal blogs. In ICAIL ’07: Proceedings of the 11th international conference on Artificial intelligence and law, pages 231–236, New York, NY, USA. ACM. [Deerwester et al., 1990] Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for information science. [Domingos and Richardson, 2001] Domingos, P. and Richardson, M. (2001). Mining the network value of customers. In KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 57–66, New York, NY, USA. ACM Press. [Drezner and Farrell, 2004] Drezner, D. and Farrell, H. (2004). The power and politics of blogs. In American Political Science Association Annual Conference. [Durrett, 1988] Durrett, R. (1988). Lecture Notes on Particle Systems and Percolation. Wadsworth Publishing. [Efimova and Hendrick, 2005] Efimova, L. and Hendrick, S. (2005). In search for a virtual settlement: An exploration of weblog community boundaries. [Elkin, ] Elkin, T. Just an online minute... online forecast. =Articles.showArticle art aid= [Fensterer, 2007] Fensterer, G. D. (2007). Planning and Assessing Stability Operations: A Proposed Value Focus Thinking Approach. PhD thesis, Air Force Institute of Technology. [Flake et al., 2002] Flake, G., Lawrence, S., Giles, C. L., and Coetzee, F. (2002). Self-organization and identification of web communities. IEEE Computer, 35(3). [Flake et al., 2000] Flake, G. W., Lawrence, S., and Giles, C. L. (2000). Efficient identification of web communities. In 6th International Conference on Knowledge Discovery and Data Mining.

References [Friedman, 2005] Friedman, T. L. (2005). The World Is Flat: A Brief History of the Twenty-First Century. Farrar, Straus and Giroux. [Gamon et al., 2005] Gamon, M., Aue, A., Corston-Oliver, S., and Ringger, E. (2005). Pulse: Mining [Gamon et al., 2008] Gamon, M., et al. (2008). BLEWS: Using Blogs to Provide Context for News Articles. In 2nd ICWSM. Customer Opinions from Free Text. In Proceedings of the 6th International Symposium on Intelligent Data Analysis. [Gibson et al., 1998] Gibson, D., Kleinberg, J., and Raghavan, P. (1998). Inferring web communities from link topology. In 9th ACM Conference on Hypertext and Hypermedia. [Gill, 2004] Gill, K. E. (2004). How can we measure the influence of the blogosphere? In Proceedings of the WWW’04: workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics. [Gillmor, 2006] Gillmor, D. (2006). We the Media: Grassroots Journalism by the People, for the People. O’Reilly. [Girvan and Newman, 2002] Girvan, M. and Newman, M. E. J. (2002). Community structure in social and biological networks. In National Academy of Science. [Gladwell, 2000] Gladwell, M. (2000). The Tipping Point: How Little Things Can Make a Big Difference. Little, Brown and Company. [Golbeck, 2006a] Golbeck, J. (2006a). Combining provenance with trust in social networks for semantic web content filtering. In IPAW, pages 101–108. [Golbeck, 2006b] Golbeck, J. (2006b). Generating predictive movie recommendations from trust in social networks. In iTrust, pages 93–104. [Golbeck et al., 2004] Golbeck, J., Bonatti, P. A., Nejdl, W., Olmedilla, D., and Winslett, M. (2004). Trust, security, and reputation on the semantic web. In Proceedings of the ISWC-04 Workshop on Trust, Security, and Reputation on the Semantic Web. [Golbeck and Hendler, 2004a] Golbeck, J. and Hendler, J. (2004a). Reputation Network Analysis for Filtering. In Proceedings Conference on and Anti-Spam (CEAS), Mountain View, USA.

References [Golbeck and Hendler, 2006] Golbeck, J. and Hendler, J. (2006). Inferring binary trust relationships in web-based social networks. ACM Trans. Inter. Tech., 6(4):497–529. [Golbeck and Hendler, 2004b] Golbeck, J. and Hendler, J. A. (2004b). Accuracy of metrics for inferring trust and reputation in semantic web-based social networks. In EKAW, pages 116–131. [Golbeck and Parsia, 2006] Golbeck, J. and Parsia, B. (2006). Trust network-based ﬁltering of aggregated claims. IJMSO, 1(1):58–65. [Golbeck et al., 2003] Golbeck, J., Parsia, B., and Hendler, J. A. (2003). Trust networks on the semantic web. In CIA, pages 238–249. [Goldenberg et al., 2001] Goldenberg, J., Libai, B., and Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12:211–223. [Gruhl et al., 2005] Gruhl, D., Guha, R., Kumar, R., Novak, J., and Tomkins, A. (2005). The predictive power of online chatter. In KDD ’05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 78–87, New York, NY, USA. ACM Press. [Gruhl et al., 2004] Gruhl, D., Liben-Nowell, D., Guha, R., and Tomkins, A. (2004). Information diﬀusion through blogspace. SIGKDD Exploration Newsletter, 6(2):43–52. [Guha et al., 2004] Guha, R., Kumar, R., Raghavan, P., and Tomkins, A. (2004). Propagation of trust and distrust. In WWW ’04: Proceedings of the 13th international conference on World Wide Web, pages 403–412, New York, NY, USA. ACM Press. [Gyongyi et al., 2006] Gyongyi, Z., Berkhin, P., Garcia-Molina, H., and Pedersen, J. (2006). Link spam detection based on mass estimation. In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB). [Gyongyi et al., 2004] Gyongyi, Z., Garcia-Molina, H., and Pedersen, J. (2004). Combating web spam with trustrank. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB). [Herring et al., 2005] Herring, S. C., Kouper, I., Paolillo, J. C., Scheidt, L. A., Tyworth, M., Welsch, P., Wright, E., and Yu, N. (2005). Conversations in the blogosphere: An analysis ”from the bottom up”. hicss, 04:107b.

References [Hopcroft et al., 2003] Hopcroft, J., Khan, O., Kulis, B., and Selman, B. (2003). Natural communities in large linked networks. In 9th Intl. Conf. on Knowledge Discovery and Data Mining. [Hope et al., 2006] Hope, T., Nishimura, T., and Takeda, H. (2006). An integrated method for social network extraction. In WWW ’06: Proceedings of the 15th international conference on World Wide Web, pages 845–846, New York, NY, USA. ACM Press. [Java et al., 2006] Java, A., Kolari, P., Finin, T., and Oates, T. (2006). Modeling the spread of influence on the blogosphere. In Proceedings of the 15th International World Wide Web Conference. [Kale et al., 2007] Kale, A., Karandikar, A., Kolari, P., Java, A., Finin, T., and Joshi, A. (2007). Modeling trust and influence in the blogosphere using link polarity. In International Conference on Weblogs and Social Media. [Katz and Golbeck, 2006] Katz, Y. and Golbeck, J. (2006). Social network-based trust in prioritized default logic. In AAAI. [Kautz et al., 1997] Kautz, H., Selman, B., and Shah, M. (1997). Referral web: combining social networks and collaborative filtering. Commun. ACM, 40(3):63–65. [Keeney and Raiffa, 1993] Keeney, R. L. and Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press. [Keller and Berry, 2003] Keller, E. and Berry, J. (2003). One American in ten tells the other nine how to vote, where to eat and, what to buy. They are The Influentials. The Free Press. [Kleinberg, 1998] Kleinberg, J. (1998). Authoritative sources in a hyperlinked environment. In 9th ACMSIAM Symposium on Discrete Algorithms. [Kolari et al., 2006a] Kolari, P., Finin, T., and Joshi, A. (2006a). SVMs for the blogosphere: Blog identification and splog detection. In AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs. [Kolari et al., 2006b] Kolari, P., Java, A., Finin, T., Oates, T., and Joshi, A. (2006b). Detecting spam blogs: A machine learning approach. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI). [Kritikopoulos et al., 2006] Kritikopoulos, A., Sideri, M., and Varlamis, I. (2006). Blogrank: ranking weblogs based on connectivity and similarity features. In AAA-IDEA ’06: Proceedings of the 2nd international workshop on Advanced architectures and algorithms for internet delivery and applications, page 8, New York, NY, USA. ACM Press.

References [Kumar et al., 2003] Kumar, R., Novak, J., Raghavan, P., and Tomkins, A. (2003). On the Bursty Evolution of Blogspace. In Proceedings of the 12th international conference on World Wide Web, pages 568–576, New York, NY, USA. ACM Press. [Kumar et al., 2006] Kumar, R., Novak, J., and Tomkins, A. (2006). Structure and evolution of online social networks. In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 611–617, New York, NY, USA. ACM Press. [Kumar et al., 1999] Kumar, R., Raghavan, P., Rajagopalan, S., and Tomkins, A. (1999). Trawling the web for emerging cyber communities. In The 8th International World Wide Web Conference. [Leshed and Kaye, 2006] Leshed, G. and Kaye, J. J. (2006). Understanding how bloggers feel: recognizing aﬀect in blog posts. In CHI ’06: CHI ’06 extended abstracts on Human factors in computing systems, pages 1019–1024, New York, NY, USA. ACM Press. [Leskovec et al., 2007] Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., and Hurst, M. (2007). Cascading behavior in large blog graphs. In SIAM International Conference on Data Mining. [Li et al., 2007] Li, B., Xu, S., and Zhang, J. (2007). Enhancing clustering blog documents by utilizing author/reader comments. In ACM-SE 45: Proceedings of the 45th annual southeast regional conference, pages 94–99, New York, NY, USA. ACM Press. [Liggett, 1985] Liggett, T. (1985). Interacting Particle Systems. Springer. [Lin et al., 2006] Lin, Y.-R., Sundaram, H., Chi, Y., Tatemura, J., and Tseng, B. (2006). Discovery of blog communities based on mutual awareness. In Proceedings of the 3rd annual workshop on webloging ecosystem: aggreation, analysis and dynamics. [Lin et al., 2007] Lin, Y.-R., Sundaram, H., Chi, Y., Tatemura, J., and Tseng, B. L. (2007). Splog detection using self-similarity analysis on blog temporal dynamics. In Proceedings of the 3rd international workshop on Adversarial information retrieval on the web (AIRWeb), pages 1–8, New York, NY, USA. ACM Press. [Liu et al., 2007] Liu, Y., Huang, X., An, A., and Yu, X. (2007). Arsa: a sentiment-aware model for predicting sales performance using blogs. In SIGIR ’07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 607–614, New York, NY, USA. ACM Press. [Marlow et al., 2006] Marlow, C., Naaman, M., Boyd, D., and Davis, M. (2006). Ht06, tagging paper, taxonomy, ﬂickr, academic article, to read. In HYPERTEXT ’06: Proceedings of the seventeenth conference on Hypertext and hypermedia, pages 31–40, New York, NY, USA. ACM Press.

References [Massa and Avesani, 2004] Massa, P. and Avesani, P. (2004). Trust-aware collaborative ﬁltering for recommender systems. In CoopIS/DOA/ODBASE, pages 492–508. [Massa and Avesani, 2005] Massa, P. and Avesani, P. (2005). Controversial users demand local trust metrics: An experimental study on epinions.com community. In AAAI, pages 121–126. [Massa and Bhattacharjee, 2004] Massa, P. and Bhattacharjee, B. (2004). Using trust in recommender systems: An experimental analysis. In iTrust, pages 221–235. [Massa and Hayes, 2005] Massa, P. and Hayes, C. (2005). Page-rerank: Using trusted links to re-rank authority. In Web Intelligence, pages 614–617. [Matsumura et al., 2005] Matsumura, N., Goldberg, D. E., and Llorà, X. (2005). Mining directed social network from message board. In WWW ’05: Special interest tracks and posters of the 14th international conference on World Wide Web, pages 1092–1093, New York, NY, USA. ACM Press. [Matsuo et al., 2006] Matsuo, Y., Mori, J., Hamasaki, M., Ishida, K., Nishimura, T., Takeda, H., Hasida, K., and Ishizuka, M. (2006). Polyphonet: an advanced social network extraction system from the web. In WWW ’06: Proceedings of the 15th international conference on World Wide Web, pages 397–406, New York, NY, USA. ACM Press. [McDonald, 2003] McDonald, D. W. (2003). Recommending collaboration with social networks: a comparative evaluation. In CHI ’03: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 593–600, New York, NY, USA. ACM Press. [McNee et al., 2002] McNee, S. M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S. K., Rashid, A. M., Konstan, J. A., and Riedl, J. (2002). On the recommending of citations for research papers. In CSCW ’02: Proceedings of the 2002 ACM conference on Computer supported cooperative work, pages 116–125, New York, NY, USA. ACM Press. [Mei et al., 2007] Mei, Q., Ling, X., Wondra, M., Su, H., and Zhai, C. (2007). Topic sentiment mixture: modeling facets and opinions in weblogs. In WWW ’07: Proceedings of the 16th international conference on World Wide Web, pages 171–180, New York, NY, USA. ACM Press.

References [Mishne and de Rijke, 2006] Mishne, G. and de Rijke, M. (2006). Deriving wishlists from blogs show us your blog, and we’ll tell you what books to buy. In Proceedings of the 15th international conference on World Wide Web, pages 925–926, New York, NY, USA. ACM Press. [Newman, 2003] Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45:167. [Newman, 2004b] Newman, M. E. J. (2004b). Fast algorithm for detecting community structure in networks. Physical Review E, 69: [Ntoulas et al., 2006] Ntoulas, A., Najork, M., Manasse, M., and Fetterly, D. (2006). Detecting spam web pages through content analysis. In Proceedings of the 15th international conference on World Wide Web (WWW). [O’Reilly, 2005] O’Reilly, T. (2005). What is Web 2.0 -design patterns and business models for the next generation of software /09/30/what-is-web20.html. [Osman and Yearwood, 2007] Osman, D. J. and Yearwood, J. L. (2007). Opinion search in web logs. In ADC ’07: Proceedings of the eighteenth conference on Australasian database, pages 133–139, Darlinghurst, Australia, Australia. Australian Computer Society, Inc. [Page et al., 1998] Page, L., Brin, S., Motwani, R., and Winograd, T. (1998). The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project. [Pennock et al., 2002] Pennock, D. M., Flake, G. W., Lawrence, S., Glover, E. J., and Giles, C. L. (2002). Winners don’t take all: Characterizing the competition for links on the web. Proceedings of the National Academy of Sciences, 99(8):5207–5211. [Pujol et al., 2002] Pujol, J. M., Sangesa, R., and Delgado, J. (2002). Extracting reputation in multi agent systems by means of social network topology. In Proceedings of the ﬁrst international joint conference on Autonomous agents and multiagent systems (AAMAS), pages 467–474, New York, NY, USA. ACM Press. [Richardson and Domingos, 2002] Richardson, M. and Domingos, P. (2002). Mining knowledge-sharing sites for viral marketing. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge Discovery and Data mining, pages 61–70, New York, NY, USA. ACM Press.

References [Sabater and Sierra, 2002] Sabater, J. and Sierra, C. (2002). Reputation and social network analysis in multi-agent systems. In AAMAS ’02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems (AAMAS), pages 475–482, New York, NY, USA. ACM Press. [Schelling, 1978] Schelling, T. (1978). Micromotives and macrobehavior norton. [Scoble and Israel, 2006] Scoble, R. and Israel, S. (2006). Naked conversations : how blogs are changing the way businesses talk with customers. John Wiley. [Spertus et al., 2005] Spertus, E., Sahami, M., and Buyukkokten, O. (2005). Evaluating similarity measures: a large-scale study in the orkut social network. In Proceeding of the eleventh ACM SIGKDD international conference on Knowledge Discovery in Data mining (KDD), pages 678–684, New York, NY, USA. ACM Press. [Stefanone et al., 2004] Stefanone, M., Hancock, J., Gay, G., and Ingraffea, A. (2004). Emergent networks, locus of control, and the pursuit of social capital. In Proceedings of the 2004 ACM conference on Computer Supported Cooperative Work (CSCW), pages 592–595, New York, NY, USA. ACM Press. [Tang et al., 2008] Tang, L., Liu, H., Zhang, J., Agarwal, N., and Salerno, J. J. (2008). Topic taxonomy adaptation for group profiling. ACM Transactions on Knowledge Discovery from Data, TKDD, 1(4). [Terveen and McDonald, 2005] Terveen, L. and McDonald, D. W. (2005). Social matching: A framework and research agenda. ACM Trans. Comput.-Hum. Interact., 12(3):401–434. [Thelwall, 2006] Thelwall, M. (2006). Bloggers under the London attacks: Top information sources and topics. In Proceedings of the 3rd annual workshop on webloging ecosystem: aggreation, analysis and dynamics. [Watts and Strogatz, 1998] Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world networks. Nature, 393(6684): [Wu et al., 2003] Wu, F., Huberman, B. A., Adamic, L. A., and Tyler, J. (2003). Information flow in social groups. [Yu and Singh, 2003] Yu, B. and Singh, M. P. (2003). Detecting deception in reputation management. In Proceedings of the second international joint conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 73–80, New York, NY, USA. ACM Press.

References [Zhang and Varadarajan, 2006] Zhang, Z. and Varadarajan, B. (2006). Utility scoring of product reviews. In Proceedings of the 15th ACM international conference on Information and Knowledge Management (CIKM), pages 51–57, New York, NY, USA. ACM Press. [Zhou and Davis, 2006] Zhou, Y. and Davis, J. (2006). Community discovery and analysis in blogspace. In Proceedings of the 15th international conference on World Wide Web, pages 1017–1018, New York, NY, USA. ACM Press. [Ziegler and Golbeck, 2007] Ziegler, C.-N. and Golbeck, J. (2007). Investigating interactions of trust and interest similarity. Decis. Support Syst., 43(2):460–475. [Ziegler and Lausen, 2004a] Ziegler, C.-N. and Lausen, G. (2004a). Paradigms for decentralized social ﬁltering exploiting trust network structure. In CoopIS/DOA/ODBASE (2), pages 840–858. [Ziegler and Lausen, 2004b] Ziegler, C.-N. and Lausen, G. (2004b). Spreading activation models for trust propagation. In Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE’04), pages 83–97, Washington, DC, USA. IEEE Computer Society. [Ziegler and Lausen, 2005] Ziegler, C.-N. and Lausen, G. (2005). Propagation models for trust and distrust in social networks. Information Systems Frontiers, 7(4-5):337–358. [Ziegler and Skubacz, 2006] Ziegler, C.-N. and Skubacz, M. (2006). Towards automated reputation and brand monitoring on the web. In WI ’06: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 1066–1072, Washington, DC, USA. IEEE Computer Society. [Sallach and Macal, 2001] Sallach, David and Macal, Charles (2001). The simulation of social agents: an introduction, Special Issue of Social Science Computer Review 19(3):245–248. [Axelrod and Tesfatsion, 2005] Axelrod, Robert and Tesfatsion, Leigh (2005). A guide for newcomers to Agent-Based Modeling in the Social Sciences, Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics

Blogosphere: Research Issues, Tools and Applications

Similar presentations

Presentation on theme: "Blogosphere: Research Issues, Tools and Applications"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Blogosphere: Research Issues, Tools and Applications

Similar presentations

Presentation on theme: "Blogosphere: Research Issues, Tools and Applications"— Presentation transcript:

Similar presentations

About project

Feedback