Social network mining, analysis, and research trends: techniques and applications
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Hershey, PA
Information Science Reference
2012
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| Online Access: | Inhaltsverzeichnis |
| Item Description: | "This book covers current research trends in the area of social networks analysis and mining, sharing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science"--Provided by publisher. Includes bibliographical references and index |
| Physical Description: | XX, 407 S. Ill., graph. Darst. |
| ISBN: | 9781613505137 9781613505151 |
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Titel: Social network mining, analysis, and research trends
Autor: Ting, I-Hsien
Jahr: 2012
Detailed Table of Contents
Foreword
XVIII
Preface
XIX
Section 1
Introduction and Surveys of Social Networks Mining and Analysis
Chapter 1
Social Multimedia Mining: Trends and Opportunities in Areas of Social
and Communication Studies.
Georgios Lappas, Technological Educational Institution of Western Macedonia, Greece
In recent years there is a vast and rapidly growing amount of multimedia content available online. Web
2.0 and online social networks have dramatically influenced the growing amount of multimedia content
due to the fact that users become more active producers and distributors of such multimedia context.
This work conceptualizes and introduces the concept of social multimedia mining as a new emerging
research area that combines web mining research, multimedia research and social media research. New
challenges in multimedia research, social network analysis research as well as trends and opportunities
in research areas of social and communication studies and more specific in politics, public relations,
public administration, marketing and advertising are discussed in this chapter.
Halil Bisgin, University of Arkansas at Little Rock, USA
Nitin Agarwal, University of Arkansas at Little Rock, USA
XiaoweiXu, University of Arkansas at Little Rock, USA
Similarity breeds connections, the principle of homophily, has been well studied in existing sociology
literature. Several studies have observed this phenomenon by conducting surveys on human subjects.
These studies have concluded that new ties are formed between similar individuals. This phenomenon has
been used to explain several socio-psychological concepts such as segregation, community development,
social mobility, etc. However, due to the nature of these studies and limitations because of involvement
of human subjects, conclusions from these studies are not easily extensible in online social media. Social
Chapter 2
A Study of Homophily on Social Media
17
media, which is becoming the infinite space for interactions, has exceeded all the expectations in terms
of growth, for reasons beyond human comprehension. New ties are formed in social media in the same
way that they emerge in the real world. However, given the differences between real-world and online
social media, do the same factors that govern the construction of new ties in the real world also govern
the construction of new ties in social media? In other words, does homophily exist in social media? In
this chapter, the authors study this highly significant question and propose a systematic approach by
studying two online social media sites, BlogCatalog and Last.fm, and report our findings along with
some interesting observations.
Chapter 3
Sociocognitive Inquiry.35
Brian R. Gaines, University of Victoria, Canada
Mildred L. G. Shaw, University of Calgary, Canada
This chapter describes techniques for sociocognitive inquiiy based on conceptual grid elicitation and
analysis using web-based tools, such as WebGrid, which are designed to elicit conceptual models from
those participating in a networked community. These techniques provide an interactive web-based ex-
perience with immediate payback from online graphic analysis, that provides an attractive alternative to,
or component of, conventional web-based surveys. In particular, they support targeted follow-up studies
based on passive data mining of the by-products of web-based community activities, allowing the phe-
nomena modeled through data mining to be investigated in greater depth. The foundations in cognitive
sociology and psychology are briefly surveyed, a case study is provided to illustrate how web-based
conceptual modeling services can be customized to integrate with a social networking site and support
a focused study, and the implications for future research are discussed.
Chapter 4
Community Discovery: From Web Pages to Social Networks.56
Damien Leprovost, University of Bourgogne, France
LyliaAbrouk, University of Bourgogne, France
David Gross-Amblard, University of Bourgogne, France
This chapter presents a state of the art of research on the discovery of Web communities, in a general
sense. For this purpose, the authors discuss various notions of communities and their related assump-
tions: hypertextual communities, tag communities and semantic-based communities.
Section 2
Measures, Methods and Techniques in Social Networks Mining and Analysis
Chapter 5
Large Scale Graph Mining with MapReduce: Diameter Estimation and Eccentricity
Plots of Massive Graphs with Mining Applications.66
Charalampos E. Tsourakakis, Carnegie Mellon University, USA
In recent years, a considerable amount of research has focused on the study of graph structures arising
from technological, biological and sociological systems. Graphs are the tool of choice in modeling such
systems since they are typically described as sets of pairwise interactions. Important examples of such
datasets are the Internet, the Web, social networks, and large-scale information networks which reach the
planetary scale, e.g., Facebook and Linkedln. The necessity to process large datasets, including graphs,
has led to a major shift towards distributed computing and parallel applications, especially in the recent
years. MapReduce was developed by Google, one of the largest users of multiple processor computing
in the world, for facilitating the development of scalable and fault tolerant applications. MapReduce has
become the de facto standard for processing large scale datasets both in industry and academia. In this
chapter, the authors present state of the art work on large scale graph mining using MapReduce. They
survey research work on an important graph mining problem, estimating the diameter of a graph and
the eccentricities/radii of its vertices. Thanks to the algorithm they present in the following, the authors
are able to mine graphs with billions of edges, and thus extract surprising patterns. The source code is
publicly available at the URL http://www.cs.cmu.edu/~pegasus/.
Chapter 6
Social Network Inspired Approach to Intelligent Monitoring of Intelligence Data.79
Qiang Shert, Aberstwyth University, UK
Tossapon Boongoen, Royal Thai Air Force Academy, Thailand
In the wake of recent terrorist atrocities, intelligence experts have commented that failures in detecting
terrorist and criminal activities are not so much due to a lack of data, as they are due to difficulties in
relating and interpreting the available intelligence. An intelligent tool for monitoring and interpreting
intelligence data will provide a helpful means for intelligence analysts to consider emerging scenarios
of plausible threats, thereby offering useful assistance in devising and deploying preventive measures
against such possibilities. One of the major problems in need of such attention is detecting false identity
that has become the common denominator of all serious crime, especially terrorism. Typical approaches
to this problem rely on the similarity measure of textual and other content-based characteristics, which
are usually not applicable in the case of deceptive and erroneous description. This barrier may be over-
come through link information presented in communication behaviors, financial interactions and social
networks. Quantitative link-based similarity measures have proven effective for identifying similar
problems in the Internet and publication domains. However, these numerical methods only concentrate
on link structures, and fail to achieve accurate and coherent interpretation of the information. Inspired by
this observation, the chapter presents a novel qualitative similarity measure that makes use of multiple
link properties to refine the underlying similarity estimation process and consequently derive semantic-
rich similarity descriptors. The approach is based on order-of-magnitude reasoning. Its performance is
empirically evaluated over a terrorism-related dataset, and compared against several state-of-the-art
link-based algorithms and other alternative methods.
Chapter 7
Detecting Pharmaceutical Spam in Microblog Messages.101
KathyJ. Liszka, University of Akron, USA
Chien-Chung Chan, University of Akron, USA
Chandra Shekar, University of Akron, USA
Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most
popular forums for microblogging in online social networks, and the fastest growing. Fifty million mes-
sages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With
this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages,
called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a
special category advertising pharmaceutical products. The authors look at the essential characteristics
of spam tweets and what makes microblogging spam unique from email or other types of spam. They
review methods and tools currently available to identify general spam tweets. Finally, this work intro-
duces a new methodology of applying text mining and data mining techniques to generate classifiers
that can be used for pharmaceutical spam detection in the context of microblogging.
Chapter 8
Exploiting Social Annotations for Resource Classification.116
Arkaitz Zubiaga, NLP IR Group, UNED, Spain
Victor Fresno, NLP IR Group, UNED, Spain
Raquel Martinez, NLP IR Group, UNED, Spain
The lack of representative textual content in many resources suggests the study of additional metadata
to improve classification tasks. Social bookmarking and cataloging sites provide an accessible way to
increase available metadata in large amounts with user-provided annotations. In this chapter, the authors
study and analyze the usefulness of social annotations for resource classification. They show that social
annotations outperform classical content-based approaches, and that the aggregation of user annotations
creates a great deal of meaningful metadata for this task. The authors also present a method to get the
most out of the studied data sources using classifier committees.
Chapter 9
Social Network Construction in the Information Age: Views and Perspectives.131
Michael Farrugia, University College Dublin, Ireland
Neil Hurley, University College Dublin, Ireland
Diane Payne, University College Dublin, Ireland
Aaron Quigley, University College Dublin, Ireland
Social scientists have been studying and refining their network data collection instruments for the last
number of decades. Data collection in this field traditionally consists of manually conducting interviews
and questionnaires on a population of interest to derive a list of ties between the members of the popula-
tion and which can later be studied from a sociological perspective. Great care and considerable resources
are often required during the research design and data collection phases in order to ensure that the final
data set is well focused, unbiased and representative of the selected population. Nowadays electronic
network data is becoming widely available and easier to access and this data brings with it a number of
advantages over manually collecting data. The ease of data collection, lower cost, large scale, temporal
information and the elimination of respondent bias and recall problems are all concrete benefits of elec-
tronic data. With these clear advantages, could electronic data be a solution to problems encountered
with manual data collection? Electronic data is often available as a bi-product of other processes (such
as phone call logs and email server logs), so often the data is not collected with the explicit purpose of
being studied from a social network perspective. This aspect shifts the design decisions on electronic
data to a later processing stage once the data is available, rather than before the data is collected. This
shift introduces a different set of decisions and processes when dealing with electronic data collection.
What are the best ways to process and interpret the data to achieve valid insights into the 'real' social
network that the social scientist is interested in? In this chapter, the authors will discuss the differences
between manual data collection and electronic data collection to understand the advantages and the
challenges brought by electronic social network data. They will discuss in detail the processes that are
used to transform electronic data to social network data and the procedures that can be used to validate
the resultant social network.
Chapter 10
Dynamics and Evolutional Patterns of Social Networks.156
Yingzi Jin, The University of Tokyo, Japan
Yutaka Matsuo, The University of Tokyo, Japan
Previous chapters focused on the models of static networks, which consider a relational network at a
given point in time. However, real-world social networks are dynamic in nature; for example, friends
of friends become friends. Social network research has, in recent years, paid increasing attention to
dynamic and longitudinal network analysis in order to understand network evolution, belief formation,
friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of
social networks. The chapter introduces real-world applications and reviews major theories and models
of dynamic network mining.
Chapter 11
Modeling Customer Behavior with Analytical Profiles.171
Jerzy Surma, Warsaw School of Economics, Poland
Contemporary companies try to build customer relationship management systems based on the customer
social relations and behavioral patterns. This is in correspondence with the current trend in marketing that
is to move from broadcast marketing operation to a one-to-one marketing. The key issue in this activity
is predicting to which products or services a particular customer was likely to respond to. In order to
build customer relationship management systems, companies have to learn to understand their customer
in the broader social context. The key hypothesis in this approach is that the predictors of behavior in
the future are customers behavior patterns in the past. This is a form of human behavioral modeling. The
individual customer behavior patterns can be used to build an analytical customer profile. This will be
described in section "Introduction" and "Customer profiling". Based on this profile a company might
target a specific customer with a personalized message. In section "Critical examples" the authors will
focus in particular on the importance of the customer social relations, that reflects referrals influence
on the marketing response. In the end in section "Market of analytical profiles" they will discuss the
potential business models related to market exchange of analytical profiles.
Chapter 12
Social Search and Personalization Through Demographic Filtering.183
Kamal Taha, Khalifa University, UAE
Ramez Elmasri, University of Texas at Arlington, USA
Most existing personalized search systems do not consider group profiling. Group profiling can be an
efficient retrieval mechanism, where a user profile is inferred from the profile of the social groups to
which the user belongs. The authors propose an XML search system called DemoFilter which employs
the concept of group profiling. DemoFilter simplifies the personalization process by pre-defining vari-
ous categories of social groups and then identifying their preferences. Social groups are characterized
based on demographic, ethnic, cultural, religious, age, or other characteristics. DemoFilter can be used
for various practical applications, such as Internet or other businesses that market preference-driven
products. In the ontology, the preferences of a social group are identified from published studies about
the social group. They experimentally evaluate the search effectiveness of DemoFilter and compare it
to an existing search engine.
Section 3
Applications and Case Studies in Social Networks Mining and Analysis
Chapter 13
Mining Organizations' Networks: Multi-Level Approach.205
James A. Danowski, University of Illinois at Chicago, USA
This chapter presents six examples of organization-related social network mining: 1 ) interorganizational
and sentiment networks in the Deepwater BP Oil Spill events, 2) intraorganizational interdepartmental
networks in the Savannah College of Art and Design (SCAD), 3) who-to-whom email networks across
the organizational hierarchy the Ford Motor Company's automotive engineering innovation: "Sync®
w/ MyFord Touch", 4) networks of selected individuals who left that organization, 5) semantic associa-
tions across email for a corporate innovation in that organization, and 6) assessment of sentiment across
its email for innovations over time. These examples are discussed in terms of motivations, methods,
implications, and applications.
Chapter 14
Universal Dynamics on Complex Networks, Really? A Comparison of Two Real-World
Networks that Cross Structural Paths.but Ever so Differently.231
Brigitte Gay, University Toulouse I, France
The complex network approach developed in statistical physics seems particularly well-suited to ana-
lyzing large networks. Progress in the study of complex networks has been made by looking for shared
properties and seemingly universal dynamics, thus ignoring the details of networks individual nodes,
links, or sub-components. Researchers now need to assess the differences in the processes that take place
on complex networks. The author first discusses briefly the theoretical understanding of evolutionary
laws governing the emeigence of these universal properties (small-world and scale-free networks) and
recent evolutions in the field of network analysis. Using data on two empirical networks, a transaction
network in the venture capital industry and an interfirm alliance network in a major sector of the bio-
pharmaceutical industry, the author then demonstrates that networks can switch from one 'universal'
structure to another, but each in its own way. This chapter highlights the need of knowing more about
networks, as 'more is different'.
Chapter 15
A Social Network Model for Understanding Technology Use for Knowledge-Intensive Workers.250
Kon Shing Kenneth Chung, University ofWollongong, Australia
This chapter presents a theoretical model based on social network theories and the social influence model
for understanding how knowledge professionals utilise technology. In particular, the association between
egocentric network properties (structure, position and tie) and information and communication technol-
ogy (ICT) use of individuals in knowledge-intensive and geographically dispersed settings is explored. A
novel triangulation methodology is adopted where in-depth interviews and observation techniques were
utilised to develop constructs for the conceptual model which were then vetted by domain-level experts.
A reliable and validated social network-based questionnaire survey is also developed to operationalise
the model. Results show that task-level ICT use is significantly associated with degree central ity and
functional tie-diversity; and communication-level ICT use is negatively associated with efficiency. The
implications of these associations for knowledge-intensive work mean that it is important to consider
the professional social network characteristics of potential users of the technology for designing ICT-
enabled organisations.
Chapter 16
Social Recommendations: Mentor and Leader Detection to Alleviate
the Cold-Start Problem in Collaborative Filtering.270
Armelle Brim, Nancy Université, France
Sylvain Castagnos, Nancy Université, France
Anne Boyer, Nancy Université, France
Recommender systems aim at suggesting to users items that fit their preferences. Collaborative filtering
is one of the most popular approaches of recommender systems; it exploits users' ratings to express
preferences. Traditional approaches of collaborative filtering suffer from the cold-start problem: when
a new item enters the system, it cannot be recommended while a sufficiently high number of users
have rated it. The quantity of required ratings is not known a priori and may be high as it depends on
who rates the items. In this chapter, the authors propose to automatically select the adequate set of us-
ers in the network of users to address the cold-start problem. They call them the "delegates", and they
correspond to those who should rate a new item first so as to reliably deduce the ratings of other users
on this item. They propose to address this issue as an opinion poll problem. The authors consider two
kinds of delegates: mentors and leaders. They experiment some measures, classically exploited in social
networks, to select the adequate set of delegates. The experiments conducted show that only 6 delegates
are sufficient to accurately estimate ratings of the whole set of other users, which dramatically reduces
the number of users classically required.
Chapter 17
Capturing Market Mavens among Advergamers: A Case of Mobile-Based
Social Networking Site in Japan.291
Shintaro Okazaki, UniversidadAutónoma de Madrid, Spain
Jaime Romero, Universidad Autónoma de Madrid, Spain
Sara Campo, Universidad Autónoma de Madrid, Spain
The objective of this chapter is to identify a market maven segment among advergamers on a mobile-
based social networking site (SNS). A real online campaign with a multiplayer game is designed for
Procter Gamble's Pringles, after which online surveys are conducted via mobile device. Finite mix-
ture models are employed to identify clusters. The estimation results suggest four clusters. The major-
ity group belongs to Clusters 1 (67%) and 2 (21%), while Clusters 3 (6.8%) and 4 (4.8%) exhibit the
propensity of market mavens. Specifically, the members of Cluster 3 are likely to have been actively
engaged in information search, purchased the sponsor brand, and disseminated their brand knowledge
of the brand, mainly through personal conversation after the game play. By contrast, the members of
Cluster 4 are unlikely to have sought information, nor to have purchased the brand after the game, but
are very likely to have spread their brand knowledge through word-of-mouth. Furthermore, they did so
via not only personal conversation but also SNS functions (i.e., messaging, blog, and discussion board).
Given this, Clusters 3 and 4 could be labeled as traditional and innovative market mavens, respectively.
Our findings suggest that online marketers should identify and incentivize market mavens by branded
entertainment so that they can then disseminate information, encourage followers, and generate a viral
chain of word-of-mouth.
Chapter 18
Semantic-Awareness for a Useful Digital Life.306
Johann Stan, Alcatel-Lucent Bell Labs, France
Myriam Ribière, Alcatel-Lucent Bell Labs, France
Jérôme Picault, Alcatel-Lucent Bell Labs, France
Lionel Natarianni, Alcatel-Lucent Bell Labs, France
Nicolas Marie, Alcatel-Lucent Bell Labs, France
In this book chapter the authors address two main challenges for building compelling social applications.
In the first challenge they focus on the user by addressing the issue of building dynamic interaction
profiles from the content they produce in a social system. Such profiles are key to find the best person
to contact based on an information need. The second challenge presents their vision of "object-centered
sociality", which allows users to create spontaneous communities centered on a digital or physical ob-
ject. In each case, proof-of-concept industrial prototypes show the potential impact of the concepts on
the daily life of users. The main contribution of this chapter is the design of conceptual frameworks for
helping users to take maximum advantage from their participation in online communities, either in the
digital web ecosystem or real-life spontaneous communities.
Chapter 19
The Comparability of Event-Related and General Social Support.339
Valentina Hlebec, University of Ljubljana, Slovenia
MajaMrzel, University of Ljubljana, Slovenia
Una Kogovsek, University of Ljubljana, Slovenia
Some studies (e.g., Kogoväek Hlebec, 2008,2009) have shown that the name generator and the role
relation approaches to measuring social networks are to some extent comparable, but less so the name
generator and the event-related approaches (Hlebec, Mrzel, Kogoväek, 2009). In this chapter, the
composition of the social support network assessed by both the general social support approach and the
event-related approach (support during 15 major life events) is analyzed and compared. In both cases, the
role relation approach is used. In addition, in both approaches a more elaborate (16 possible categories
ranging from partner, mother, father, friend to no one) and a more simple (6 possible categories ranging
from family member, friend, neighbor to no one) response format is applied and compared. The aim of
the chapter is to establish, in a controlled quasi-experiment setting, whether the different approaches (i.e.
the general social support and the event-related approach) produce similar social networks regardless of
the response format (long vs. short).
Compilation of References.360
About the Contributors.393
Index
403 |
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| building | Verbundindex |
| bvnumber | BV039842076 |
| callnumber-first | H - Social Science |
| callnumber-label | HM742 |
| callnumber-raw | HM742 |
| callnumber-search | HM742 |
| callnumber-sort | HM 3742 |
| callnumber-subject | HM - Sociology |
| ctrlnum | (OCoLC)754105668 (DE-599)BVBBV039842076 |
| dewey-full | 302.30285 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 302 - Social interaction |
| dewey-raw | 302.30285 |
| dewey-search | 302.30285 |
| dewey-sort | 3302.30285 |
| dewey-tens | 300 - Social sciences |
| discipline | Soziologie |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV039842076 |
| illustrated | Illustrated |
| indexdate | 2024-12-05T15:03:44Z |
| institution | BVB |
| isbn | 9781613505137 9781613505151 |
| language | English |
| lccn | 2011038347 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024701967 |
| oclc_num | 754105668 |
| open_access_boolean | |
| owner | DE-12 |
| owner_facet | DE-12 |
| physical | XX, 407 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Information Science Reference |
| record_format | marc |
| spelling | Social network mining, analysis, and research trends techniques and applications I-Hsien Ting, Tzung-Pei Hong and Leon S.L. Wang, eds. Hershey, PA Information Science Reference 2012 XX, 407 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier "This book covers current research trends in the area of social networks analysis and mining, sharing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science"--Provided by publisher. Includes bibliographical references and index Online social networks Data mining Soziales Netzwerk (DE-588)4055762-5 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Social Media (DE-588)4639271-3 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Soziales Netzwerk (DE-588)4055762-5 s Social Media (DE-588)4639271-3 s Data Mining (DE-588)4428654-5 s DE-604 Ting, I-Hsien Sonstige oth Erscheint auch als Online-Ausgabe 978-1-61350-514-4 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024701967&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Social network mining, analysis, and research trends techniques and applications Online social networks Data mining Soziales Netzwerk (DE-588)4055762-5 gnd Data Mining (DE-588)4428654-5 gnd Social Media (DE-588)4639271-3 gnd |
| subject_GND | (DE-588)4055762-5 (DE-588)4428654-5 (DE-588)4639271-3 (DE-588)4143413-4 |
| title | Social network mining, analysis, and research trends techniques and applications |
| title_auth | Social network mining, analysis, and research trends techniques and applications |
| title_exact_search | Social network mining, analysis, and research trends techniques and applications |
| title_full | Social network mining, analysis, and research trends techniques and applications I-Hsien Ting, Tzung-Pei Hong and Leon S.L. Wang, eds. |
| title_fullStr | Social network mining, analysis, and research trends techniques and applications I-Hsien Ting, Tzung-Pei Hong and Leon S.L. Wang, eds. |
| title_full_unstemmed | Social network mining, analysis, and research trends techniques and applications I-Hsien Ting, Tzung-Pei Hong and Leon S.L. Wang, eds. |
| title_short | Social network mining, analysis, and research trends |
| title_sort | social network mining analysis and research trends techniques and applications |
| title_sub | techniques and applications |
| topic | Online social networks Data mining Soziales Netzwerk (DE-588)4055762-5 gnd Data Mining (DE-588)4428654-5 gnd Social Media (DE-588)4639271-3 gnd |
| topic_facet | Online social networks Data mining Soziales Netzwerk Data Mining Social Media Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024701967&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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