Emergent Data Mining Tools for Social Network Analysis

2015 ◽  
pp. 1539-1556
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Alex Takata
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Online Social Networks ◽  
Data Extraction ◽  
Future Directions ◽  
Effective Development ◽  
Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

Emergent Data Mining Tools for Social Network Analysis

2013 ◽  
pp. 40-57
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Alex Takata
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Online Social Networks ◽  
Data Extraction ◽  
Future Directions ◽  
Effective Development ◽  
Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

Detecting Pharmaceutical Spam in Microblog Messages

2011 ◽  
pp. 101-115
Author(s):  
Kathy J. Liszka ◽  
Chien-Chung Chan ◽  
Chandra Shekar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Cell Phones ◽  
Pharmaceutical Products ◽  
Spam Detection ◽  
Data Mining Techniques ◽  
Flow Through ◽  
Special Category

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 messages 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 introduces 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.

Case Study

2021 ◽  
pp. 345-366
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
User Research ◽  
Social Games ◽  
Work Done

This chapter discusses Social Network Analysis, a technique used to analyze social networks within social games as a method to enhance retention in games. We will show how one can use this method by applying it to the problem of retention within the game Tom Clancy’s The Division (TCTD). Using the game and the analysis will help you understand how to use SNA to understand types of players and influential players, and, as a result, understand how to engage different players, especially influencers, to increase retention. While the chapter will focus on the use of SNA for TCTD as a case study, the methods discussed under SNA can be applied to other types of games. Please note that this chapter is an extension of the work done by several collaborators to the authors, including Casper Harteveld (professor, Northeastern University), Sebastian Deterding (professor, York University), and Ahmad Azadvar (User Research Lead at Ubisoft Massive), and the work was accomplished with the support of Ubisoft, the Games Lab, and the Live Ops team at Massive Entertainment.

The Alt-social Network of Natalie Bookchin’s Testament

2016 ◽  
Vol 18 (5) ◽  
pp. 459-477
Author(s):  
Sarah Whitcomb Laiola
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Web ◽  
User Interactions ◽  
Media Criticism ◽  
Direct Translation ◽  
The Social

This article addresses issues of user precarity and vulnerability in online social networks. As social media criticism by Jose van Dijck, Felix Stalder, and Geert Lovink describes, the social web is a predatory system that exploits users’ desires for connection. Although accurate, this critical description casts the social web as a zone where users are always already disempowered, so fails to imagine possibilities for users beyond this paradigm. This article examines Natalie Bookchin’s composite video series, Testament, as it mobilizes an alt-(ernative) social network of vernacular video on YouTube. In the first place, the alt-social network works as an iteration of “tactical media” to critically reimagine empowered user-to-user interactions on the social web. In the second place, it obfuscates YouTube’s data-mining functionality, so allows users to socialize online in a way that evades their direct translation into data and the exploitation of their social labor.

Data mining and social web semantics: a case study on the use of hashtags and memes in Online Social Networks

2017 ◽  
Vol 15 (12) ◽  
pp. 2276-2281 ◽  
Author(s):  
Pedro Pinto ◽  
Ingrhid Theodoro ◽  
Marcos Arrais ◽  
Jonice Oliveira
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Online Social Networks ◽  
Social Web

Hadoop-Based Distributed K-Shell Decomposition for Social Networks

2018 ◽  
pp. 125-145
Author(s):  
Katerina Pechlivanidou ◽  
Dimitrios Katsaros ◽  
Leandros Tassiulas
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Complex Network ◽  
Online Social Networks ◽  
Distributed Algorithm ◽  
Data Centers ◽  
Large Data ◽  
Complex Network Analysis ◽  
Centrality Analysis

Complex network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a network is a technique that has been used for centrality analysis, for communities' discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run, i.e., large data centers, makes none of the existing algorithms appropriate for the decomposition of graphs into shells. In this article, we develop for a distributed algorithm based on MapReduce for the k-shell decomposition of a graph. We furthermore, provide an implementation and assessment of the algorithm using real social network datasets. We analyze the tradeoffs and speedup of the proposed algorithm and conclude for its virtues and shortcomings.

Data Mining for Social Network Analysis Using a CLIQUE Algorithm

2019 ◽  
pp. 160-187
Author(s):  
Phu Ngoc Vo ◽  
Tran Vo Thi Ngoc
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Social ◽  
The World ◽  
Commercial Applications ◽  
Clique Algorithm

Many different areas of computer science have been developed for many years in the world. Data mining is one of the fields which many algorithms, methods, and models have been built and applied to many commercial applications and research successfully. Many social networks have been invested and developed in the strongest way for the recent years in the world because they have had many big benefits as follows: they have been used by lots of users in the world and they have been applied to many business fields successfully. Thus, a lot of different techniques for the social networks have been generated. Unsurprisingly, the social network analysis is crucial at the present time in the world. To support this process, in this book chapter we have presented many simple concepts about data mining and social networking. In addition, we have also displayed a novel model of the data mining for the social network analysis using a CLIQUE algorithm successfully.

Detecting Pharmaceutical Spam in Microblog Messages

Data Mining ◽  
2013 ◽  
pp. 1407-1420
Author(s):  
Kathy J. Liszka ◽  
Chien-Chung Chan ◽  
Chandra Shekar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Cell Phones ◽  
Pharmaceutical Products ◽  
Spam Detection ◽  
Data Mining Techniques ◽  
Flow Through ◽  
Special Category

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 messages 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 introduces 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.

Social Network Analysis

2018 ◽  
pp. 1-18 ◽  
Author(s):  
Somya Jain ◽  
Adwitiya Sinha
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Analysis Techniques ◽  
Text Images ◽  
Application Specific

Over the last decade, technology has thrived to provide better, quicker, and more effective platforms to help individuals connect and disseminate information to other individuals. The increasing popularity of these networks and its huge content in the form of text, images, and videos provides new opportunities for data analytics in the context of social networks. This motivates data mining experts and researchers to deploy various mining apparatus and application-specific tools for analysing the massive, intricate, and dynamic social media knowledge. The research detailed in this chapter would entail major social network concepts with data analysis techniques. Moreover, it gives insight to representation and modelling of social networks with research datasets and tools.

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