Exploring Virtual Communities with the Internet Community Text Analyzer (ICTA)

2011 ◽  
pp. 205-223 ◽  
Author(s):  
Anatoliy Gruzd
Keyword(s):  
Social Network ◽  
Virtual Communities ◽  
Virtual Community ◽  
Automated Analysis ◽  
Network Data ◽  
Web Based ◽  
Social Network Data ◽  
Network Discovery ◽  
The Social ◽  
Web Based System

The chapter presents a new web-based system called ICTA (http://netlytic.org) for automated analysis and visualization of online conversations in virtual communities. ICTA is designed to help researchers and other interested parties derive wisdom from large datasets. The system does this by offering a set of text mining techniques coupled with useful visualizations. The first part of the chapter describes ICTA’s infrastructure and user interface. The second part discusses two social network discovery procedures used by ICTA with a particular focus on a novel content-based method called name networks. The main advantage of this method is that it can be used to transform even unstructured Internet data into social network data. With the social network data available it is much easier to analyze, and make judgments about, social connections in a virtual community.

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

scholarly journals Designing Educational Analytics Tools Using Visualization Based on Behavioral, Motivational, and Social Network Data

2019 ◽  
pp. 81-93
Author(s):  
Iliya L. Musabirov ◽  
Keyword(s):  
Social Network ◽  
Social Values ◽  
Network Data ◽  
Network Approach ◽  
Social Network Data ◽  
Use Of Data ◽  
University Courses ◽  
The Social ◽  
Social Network Approach ◽  
Visualization Tools

The article presents a description of the approach to the use of data visualization in various educational Analytics tools when building University courses. In addition to the analysis of educational behavior, socio-psychological approaches, including the theory of expectations and social values, and the social network approach, are separately considered as prospects for analysis. An example of designing training Analytics using modern data analysis and visualization tools is analyzed.

Social Network Analysis in Marketing

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Social Network Analysis in Marketing

2011 ◽  
pp. 271-283
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Genetic Algorithm Applied in the Social Network Data Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 2188-2191
Author(s):  
Lei Liu ◽  
Quan Bao Gao
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Data Analysis ◽  
Rapid Development ◽  
Virtual Community ◽  
Social Network Data ◽  
Starting Point ◽  
The Social ◽  
Optimal Data Analysis

The rapid development of network and information technology makes the network become the indispensable part in people's life. Network design uses email as a starting point, instead of actual letters. Then Happy Nets, BBS etc. are evolved from it, with virtual as their major feature. In the process of social networks evolution, the personal image transformed from the actual into the virtual one. All this has contributed to the birth of the social network, which then makes the contacts among people presenting the feature of network expansion and cost reduction. The popular social network nowadays is considered to be social plus network, namely, through the network, as a carrier, people are connected to form a virtual community with certain characteristics. Based on the genetic algorithm and genetic coding technology, the article is designed to make the optimal data analysis and create a optimistic cyber environment in the process of the social networks explosive development.

Assessing the Social Network Health of Virtual Communities

2011 ◽  
pp. 715-730
Author(s):  
David Hinds ◽  
Ronald M. Lee
Keyword(s):  
Social Network ◽  
Virtual Communities ◽  
Virtual Community ◽  
Social Capital Theory ◽  
Software Project ◽  
Capital Theory ◽  
Community Archives ◽  
The Social ◽  
The Status ◽  
Development And Validation

In this chapter, the authors suggest how measures of “social network health” can be used to evaluate the status and progress of a virtual community. Using social capital theory as a foundation, the authors describe community health as the general condition of a community leading toward its advancement or decline, and show how social network analytical measures can be applied to existing virtual community archives to measure social network health. They describe the metric development and validation process and use their empirical study of 143 open source software project communities to illustrate how this process can be applied. Their hope is social network health metrics will be devised and integrated into host platforms for various types of virtual communities, thus providing socio-technical system designers and community managers with a valuable new diagnostic tool for tracking the status and progress of their communities.

A Conceptual Framework for Social Network Data Security

2013 ◽  
pp. 58-86 ◽  
Author(s):  
Sanur Sharma ◽  
Vishal Bhatnagar
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Conceptual Framework ◽  
Social Networking Sites ◽  
Community Analysis ◽  
Network Data ◽  
Data Mining Techniques ◽  
Social Network Data ◽  
The Social ◽  
Using Data

In recent times, there has been a tremendous increase in the number of social networking sites and their users. With the amount of information posted on the public forums, it becomes essential for the service providers to maintain the privacy of an individual. Anonymization as a technique to secure social network data has gained popularity, but there are challenges in implementing it effectively. In this chapter, the authors have presented a conceptual framework to secure the social network data effectively by using data mining techniques to perform in-depth social network analysis before carrying out the actual anonymization process. The authors’ framework in the first step defines the role of community analysis in social network and its various features and temporal metrics. In the next step, the authors propose the application of those data mining techniques that can deal with the dynamic nature of social network and discover important attributes of the social network. Finally, the authors map their security requirements and their findings of the network properties which provide an appropriate base for selection and application of the anonymization technique to protect privacy of social network data.

Data Preprocessing for Dynamic Social Network Analysis

2013 ◽  
pp. 25-39 ◽  
Author(s):  
Preeti Gupta ◽  
Vishal Bhatnagar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Preprocessing ◽  
Original Data ◽  
Network Data ◽  
Data Set ◽  
Social Network Data ◽  
Dynamic Social Network ◽  
The Social

The social network analysis is of significant interest in various application domains due to its inherent richness. Social network analysis like any other data analysis is limited by the quality and quantity of data and for which data preprocessing plays the key role. Before the discovery of useful information or pattern from the social network data set, the original data set must be converted to a suitable format. In this chapter we present various phases of social network data preprocessing. In this context, the authors discuss various challenges in each phase. The goal of this chapter is to illustrate the importance of data preprocessing for social network analysis.

Using Social Network Data to Identify Key Influencers for Social CRM Activities

2013 ◽  
pp. 256-272
Author(s):  
Goetz Greve
Keyword(s):  
Social Network ◽  
Performance Measures ◽  
Implementation Process ◽  
Social Effects ◽  
Network Data ◽  
Network Marketing ◽  
Social Network Data ◽  
Social Crm ◽  
The Social ◽  
The Relationship

Social network data can be used to identify key influencers within a company’s customer database. Key influencers are consumers that are equipped with a large and strong network of connected neighbors. Within such a strong network, marketing messages can be passed on easily via the key influencers. The purpose of the chapter is to elaborate on the social effects of customer networks and the possibility to use data from these networks for Social CRM. First, the foundations of social contagion in networks and the relationship between social effects and Social CRM performance measures are explained. Second, possible ways of data acquisition and data integration are discussed and an overview of analytical software solutions is given. Fourth, the implementation process and its challenges are elaborated. The chapter closes with an outline of further research directions.

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