A Study of Friendship Networks and Blogosphere

A Study of Friendship Networks and Blogosphere

Nitin Agarwal (Arizona State University, USA), Huan Liu (Arizona State University, USA) and Jianping Zhang (MITRE Corporation, USA)
Copyright: © 2009 |Pages: 24
DOI: 10.4018/978-1-59904-990-8.ch036
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Abstract

In Golbeck and Hendler (2006), authors consider those social friendship networking sites where users explicitly provide trust ratings to other members. However, for large social friendship networks it is infeasible to assign trust ratings to each and every member so they propose an inferring mechanism which would assign binary trust ratings (trustworthy/non-trustworthy) to those who have not been assigned one. They demonstrate the use of these trust values in e-mail ?ltering application domain and report encouraging results. Authors also assume three crucial properties of trust for their approach to work: transitivity, asymmetry, and personalization. These trust scores are often transitive, meaning, if Alice trusts Bob and Bob trusts Charles then Alice can trust Charles. Asymmetry says that for two people involved in a relationship, trust is not necessarily identical in both directions. This is contrary to what was proposed in Yu and Singh (2003). They assume symmetric trust values in the social friendship network. Social networks allow us to share experiences, thoughts, opinions, and ideas. Members of these networks, in return experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their needs will be met through being together. Individuals expand their social networks, convene groups of like-minded individuals and nurture discussions. In recent years, computers and the World Wide Web technologies have pushed social networks to a whole new level. It has made possible for individuals to connect with each other beyond geographical barriers in a “flat” world. The widespread awareness and pervasive usability of the social networks can be partially attributed to Web 2.0. Representative interaction Web services of social networks are social friendship networks, the blogosphere, social and collaborative annotation (aka “folksonomies”), and media sharing. In this work, we brie?y introduce each of these with focus on social friendship networks and the blogosphere. We analyze and compare their varied characteristics, research issues, state-of-the-art approaches, and challenges these social networking services have posed in community formation, evolution and dynamics, emerging reputable experts and in?uential members of the community, information diffusion in social networks, community clustering into meaningful groups, collaboration recommendation, mining “collective wisdom” or “open source intelligence” from the exorbitantly available user-generated contents. We present a comparative study and put forth subtle yet essential differences of research in friendship networks and Blogosphere, and shed light on their potential research directions and on cross-pollination of the two fertile domains of ever expanding social networks on the Web.
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Introduction

For many years psychologists, anthropologists and behavioral scientists have studied the societal capabilities of humans. They present several studies and results that substantiate the fact that humans like engaging themselves in complex social relationships and admire being a part of social groups. People form communities and groups for the same reasons to quench the thirst for social interaction. Often these groups have like minded members or people with similar interests who discuss various issues including politics, economics, technology, life style, entertainment and what not. These discussions could be between two members of the group or involve several members.

These social interactions also led researchers to hypothesize “Small World Phenomenon” (also known as “Small World Effect”) which states that everyone in this world can be contacted via a short chain of social acquaintances. A renowned experiment conducted by psychologist Stanley Milgram in 1967 to find the length of this short chain resulted in the discovery of very interesting observations. This finding gave rise to the famous concept, “six degrees of separation”1 . Milgram asked his subjects to send mails through US Post and keep passing them until they reached the destination. A more recent experiment conducted in 2001 by Duncan Watts, a professor at Columbia University also concluded with similar results although on a worldwide scale including 157 countries using e-mails and internet as the medium for message passing. This “connectedness” aspect of social interactions between people have fascinated several researchers and results have been applied to fields as varied as genealogy studies.

Several sociologists have pointed out subtle differences between society and community, community being a more cohesive entity that promotes a sense of security and freedom among its members. With continued communication, members develop emotional bonds, intellectual pathways, enhanced linguistic abilities, critical thinking and a knack for problem solving. Researchers in the field of psycho-analysis have studied how these interactions within a community proceed and how a group evolves over time. This line of research deals more with the group dynamics and social behavior of communities as a whole with respect to each individual. Several anthropologists are also interested in groups that are bound by cultural ties and try to study their differences from traditional groups in aspects like, communication styles, evolution patterns, participation and involvement, etc.

For the past 15 years Computers and Internet have revolutionized the way people communicate. Internet has made possible for people to connect with each other beyond all geographical barriers. This has tremendously affected social interactions between people and communities. People not only participate in regional issues but also global issues. They can connect to people sitting on exactly the other side of the globe and discuss whatever they like, i.e., living in a flat world. Communities can be spread across several time zones. This humongous mesh of social interactions is termed as social network. Social networks encompass interactions between different people, members of a community or members across different communities. Each person in this social network is represented as node and the communications represent the links or edges among these nodes. A social network comprises of several focussed groups or communities that can be treated as subgraphs. These social networks and subgraphs are highly dynamic in nature which has fascinated several researchers to study the structural and temporal characteristics of social networks. These social interactions could take one of the following forms: friendship networks, blogosphere, media sharing and social and collaborative annotation or “folksonomy”. Next we explain each of these in detail. However, in this chapter we will focus on two special types of social networking phenomena: social friendship networks and blogosphere.

Key Terms in this Chapter

Blogosphere: A special class of social networks that exhibit a ?exible graph structure among members of the network, supporting public discussion and interaction among community members. These are person-to-group interaction structures. There is no concept of private interaction. These social networks are predominantly used for sharing opinions and ideas with a community rather than a single individual. It is also de?ned as the universe of all blog sites.

Social Network: An association of entities like people, organizations drawn together by one or more speci?c types of relations, such as friendship, kinship, like or dislike, ?nancial exchange, etc. Such a social structure is often modeled using graphs, where members or actors of social networks act as the nodes and their interactions or relationships form the edges. Social networks encompass interactions between different people, members of a community or members across different communities.

Folksonomy: It is a collaboratively generated taxonomic structure of Web pages, media like hyperlinks, images and movies using open-ended labels called tags. Folksonomies make information increasingly easy to search, discover and navigate over time. The descriptive content of such a tagging process is considered better than automatic tagging because of the “collective wisdom” and better context handling capabilities of humans as compared to computing algorithms.

Blog Post: Web entries that are published on a blog site are called blog posts.

Blog: The term “blog” is derived from the word “Web-log”, which means a Web site that displays in reverse chronological order the entries by one or more individuals and usually has links to comments on speci?c postings.

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Editorial Advisory Board
Table of Contents
Foreword
Xiaohua Hu
Preface
Min Song, Yi-Fang Brook Wu
Acknowledgment
Min Song, Yi-Fang Brook Wu
Chapter 1
Ying Liu
In the automated text classification, a bag-of-words representation followed by the tfidf weighting is the most popular approach to convert the... Sample PDF
On Document Representation and Term Weights in Text Classification
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Chapter 2
Yi-fang Brook Wu, Quanzhi Li
Document keyphrases provide semantic metadata which can characterize documents and produce an overview of the content of a document. This chapter... Sample PDF
Deriving Document Keyphrases for Text Mining
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Chapter 3
John Atkinson
This chapter introduces a novel evolutionary model for intelligent text mining. The model deals with issues concerning shallow text representation... Sample PDF
Intelligent Text Mining: Putting Evolutionary Methods and Language Technologies Together
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Chapter 4
Xiaoyan Yu, Manas Tungare, Weigo Yuan, Yubo Yuan, Manuel Pérez-Quiñones, Edward A. Fox
Syllabi are important educational resources. Gathering syllabi that are freely available and creating useful services on top of the collection... Sample PDF
Automatic Syllabus Classification Using Support Vector Machines
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Chapter 5
Xiao-Li Li
In traditional text categorization, a classifier is built using labeled training documents from a set of predefined classes. This chapter studies a... Sample PDF
Partially Supervised Text Categorization
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Chapter 6
Yu-Jin Zhang
Mining techniques can play an important role in automatic image classification and content-based retrieval. A novel method for image classification... Sample PDF
Image Classification and Retrieval with Mining Technologies
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Chapter 7
Han-joon Kim
This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve... Sample PDF
Improving Techniques for Naïve Bayes Text Classifiers
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Chapter 8
Ricco Rakotomalala, Faouzi Mhamdi
In this chapter, we are interested in proteins classification starting from their primary structures. The goal is to automatically affect proteins... Sample PDF
Using the Text Categorization Framework for Protein Classification
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Chapter 9
Wilson Wong
Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However... Sample PDF
Featureless Data Clustering
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Chapter 10
Xiaohui Cui
In this chapter, we introduce three nature inspired swarm intelligence clustering approaches for document clustering analysis. The major challenge... Sample PDF
Swarm Intelligence in Text Document Clustering
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Chapter 11
P. Viswanth
Clustering is a process of finding natural grouping present in a dataset. Various clustering methods are proposed to work with various types of... Sample PDF
Some Efficient and Fast Approaches to Document Clustering
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Chapter 12
Abdelmalek Amine, Zakaria Elberrichi, Michel Simonet, Ladjel Bellatreche, Mimoun Malki
The classification of textual documents has been the subject of many studies. Technologies like the Web and numerical libraries facilitated the... Sample PDF
SOM-Based Clustering of Textual Documents Using WordNet
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Chapter 13
Lean Yu, Shouyang Wang, Kin Keung Lai
With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some... Sample PDF
A Multi-Agent Neural Network System for Web Text Mining
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Chapter 14
Sangeetha Kutty
With the emergence of XML standardization, XML documents have been widely used and accepted in almost all the major industries. As a result of the... Sample PDF
Frequent Mining on XML Documents
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Chapter 15
Richi Nayak
XML has gained popularity for information representation, exchange and retrieval. As XML material becomes more abundant, its heterogeneity and... Sample PDF
The Process and Application of XML Data Mining
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Chapter 16
Francesco Buccafurri
In the context of Knowledge Discovery in Databases, data reduction is a pre-processing step delivering succinct yet meaningful data to sequent... Sample PDF
Approximate Range Querying over Sliding Windows
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Chapter 17
Jan H. Kroeze
This chapter discusses the application of some data warehousing techniques on a data cube of linguistic data. The results of various modules of... Sample PDF
Slicing and Dicing a Linguistic Data Cube
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Chapter 18
Yi-fang Brook Wu, Xin Chen
This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules... Sample PDF
Discovering Personalized Novel Knowledge from Text
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Chapter 19
Catia Pesquita
Biomedical research generates a vast amount of information that is ultimately stored in scientific publications or in databases. The information in... Sample PDF
Untangling BioOntologies for Mining Biomedical Information
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Chapter 20
Luis M. de Campos
In this chapter, we present a thesaurus application in the field of text mining and more specifically automatic indexing on the set of descriptors... Sample PDF
Thesaurus-Based Automatic Indexing
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Chapter 21
Concept-Based Text Mining  (pages 346-358)
Stanley Loh, Leandro Krug Wives, Daniel Lichtnow, José Palazzo M. de Oliveira
The goal of this chapter is to present an approach to mine texts through the analysis of higher level characteristics (called “concepts’)... Sample PDF
Concept-Based Text Mining
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Chapter 22
Marcello Pecoraro
This chapter aims at providing an overview about the use of statistical methods supporting the Web Usage Mining. Within the first part is described... Sample PDF
Statistical Methods for User Profiling in Web Usage Mining
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Chapter 23
Quanzhi Li, Yi-fang Brook Wu
This chapter presents a new approach of mining the Web to identify people of similar background. To find similar people from the Web for a given... Sample PDF
Web Mining to Identify People of Similar Background
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Chapter 24
Pawan Lingras
This chapter describes how Web usage patterns can be used to improve the navigational structure of a Web site. The discussion begins with an... Sample PDF
Hyperlink Structure Inspired by Web Usage
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Chapter 25
Rosa Meo, Maristella Matera
In this chapter, we present the usage of a modeling language, WebML, for the design and the management of dynamic Web applications. WebML also makes... Sample PDF
Designing and Mining Web Applications: A Conceptual Modeling Approach
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Chapter 26
Brigitte Trousse, Marie-Aude Aufaure, Bénédicte Le Grand, Yves Lechevallier, Florent Masseglia
This chapter proposes an original approach for ontology management in the context of Web-based information systems. Our approach relies on the usage... Sample PDF
Web Usage Mining for Ontology Management
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Chapter 27
Yue-Shi Lee
Web mining is one of the mining technologies, which applies data mining techniques in large amounts of Web data to improve the Web services. Web... Sample PDF
A Lattice-Based Framework for Interactively and Incrementally Mining Web Traversal Patterns
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Chapter 28
Stanley R.M. Oliveira, Osmar R. Zaïane
Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues... Sample PDF
Privacy-Preserving Data Mining on the Web: Foundations and Techniques
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Chapter 29
G.S. Mahalakshmi, S. Sendhilkumar
Automatic reference tracking involves systematic tracking of reference articles listed for a particular research paper by extracting the references... Sample PDF
Automatic Reference Tracking
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Chapter 30
Wilson Wong
As more electronic text is readily available, and more applications become knowledge intensive and ontology-enabled, term extraction, also known as... Sample PDF
Determination of Unithood and Termhood for Term Recognition
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Chapter 31
Fotis Lazarinis
Over 60% of the online population are non-English speakers and it is probable the number of non-English speakers is growing faster than English... Sample PDF
Retrieving Non-Latin Information in a Latin Web: The Case of Greek
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Chapter 32
Anne Kao
Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text... Sample PDF
Latent Semantic Analysis and Beyond
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Chapter 33
Ganesh Ramakrishnan, Pushpak Bhattacharyya
Text mining systems such as categorizers and query retrievers of the first generation were largely hinged on word level statistics and provided a... Sample PDF
Question Answering Using Word Associations
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Chapter 34
Giuseppe Manco, Riccardo Ortale, Andrea Tagarelli
Personalization is aimed at adapting content delivery to users’ profiles: namely, their expectations, preferences and requirements. This chapter... Sample PDF
The Scent of a Newsgroup: Providing Personalized Access to Usenet Sites through Web Mining
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Chapter 35
Alexander Dreweke, Ingrid Fischer, Tobias Werth, Marc Wörlein
Searching for frequent pieces in a database with some sort of text is a well-known problem. A special sort of text is program code as e.g. C++ or... Sample PDF
Text Mining in Program Code
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Chapter 36
Nitin Agarwal, Huan Liu, Jianping Zhang
In Golbeck and Hendler (2006), authors consider those social friendship networking sites where users explicitly provide trust ratings to other... Sample PDF
A Study of Friendship Networks and Blogosphere
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Chapter 37
Pasquale De Meo
In this chapter we present an information system conceived for supporting managers of Public Health Care Agencies to decide the new health care... Sample PDF
An HL7-Aware Decision Support System for E-Health
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Chapter 38
Diego Liberati
Building effective multitarget classifiers is still an on-going research issue: this chapter proposes the use of the knowledge gleaned from a human... Sample PDF
Multitarget Classifiers for Mining in Bioinformatics
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Chapter 39
Shuting Xu
Text mining is an instrumental technology that today’s organizations can employ to extract information and further evolve and create valuable... Sample PDF
Current Issues and Future Analysis in Text Mining for Information Security Applications
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Chapter 40
E. Thirumaran
This chapter introduces Collaborative filtering-based recommendation systems, which has become an integral part of E-commerce applications, as can... Sample PDF
Collaborative Filtering Based Recommendation Systems
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Chapter 41
Hanna Suominen
The purpose of this chapter is to provide an overview of prevalent measures for evaluating the quality of system output in seven key text mining... Sample PDF
Performance Evaluation Measures for Text Mining
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Chapter 42
Yanliang Qi
The biology literatures have been increased in an exponential growth in recent year. The researchers need an effective tool to help them find out... Sample PDF
Text Mining in Bioinformatics: Research and Application
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Chapter 43
Ki Jung Lee
With the increased use of Internet, a large number of consumers first consult on line resources for their healthcare decisions. The problem of the... Sample PDF
Literature Review in Computational Linguistics Issues in the Developing Field of Consumer Informatics: Finding the Right Information for Consumer's Health Information Need
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Chapter 44
Richard S. Segall
This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software... Sample PDF
A Survey of Selected Software Technologies for Text Mining
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Chapter 45
Ah Chung Tsoi, Phuong Kim To, Markus Hagenbuchner
This chapter describes the application of a number of text mining techniques to discover patterns in the health insurance schedule with an aim to... Sample PDF
Application of Text Mining Methodologies to Health Insurance Schedules
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Chapter 46
Miao-Ling Wang, Hsiao-Fan Wang
With the ever-increasing and ever-changing flow of information available on the Web, information analysis has never been more important. Web text... Sample PDF
Web Mining System for Mobile-Phone Marketing
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Chapter 47
Neil Davis
Text mining technology can be used to assist in finding relevant or novel information in large volumes of unstructured data, such as that which is... Sample PDF
Web Service Architectures for Text Mining: An Exploration of the Issues via an E-Science Demonstrator
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