Social Network Analytics

Social Network Analytics
Author: Nilanjan Dey,Samarjeet Borah,Rosalina Babo,Amira S. Ashour
Publsiher: Academic Press
Total Pages: 267
Release: 2018-11-16
ISBN 10: 0128156414
ISBN 13: 9780128156414
Language: EN, FR, DE, ES & NL

Social Network Analytics Book Review:

Social Network Analytics: Computational Research Methods and Techniques focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging area of research. In addition, it includes a variety of applications from several domains, such as scientific research, and the business and industrial sectors. The technical aspects of analysis are covered in detail, including visualizing and modeling, network theory, mathematical models, the big data analytics of social networks, multidimensional scaling, and more. As analyzing social network data is rapidly gaining interest in the scientific research community because of the importance of the information and insights that can be culled from the wealth of data inherent in the various aspects of the network, this book provides insights on measuring the relationships and flows between people, groups, organizations, computers, URLs, and more. Examines a variety of data analytic techniques that can be applied to social networks Discusses various methods of visualizing, modeling and tracking network patterns, organization, growth and change Covers the most recent research on social network analysis and includes applications to a number of domains

Social Network Data Analytics

Social Network Data Analytics
Author: Charu C. Aggarwal
Publsiher: Springer Science & Business Media
Total Pages: 502
Release: 2011-03-18
ISBN 10: 1441984623
ISBN 13: 9781441984623
Language: EN, FR, DE, ES & NL

Social Network Data Analytics Book Review:

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Big Data Analytics

Big Data Analytics
Author: Mrutyunjaya Panda,Ajith Abraham,Aboul Ella Hassanien
Publsiher: CRC Press
Total Pages: 316
Release: 2018-12-12
ISBN 10: 1351622595
ISBN 13: 9781351622592
Language: EN, FR, DE, ES & NL

Big Data Analytics Book Review:

Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

Social Network Analytics for Contemporary Business Organizations

Social Network Analytics for Contemporary Business Organizations
Author: Bansal, Himani,Shrivastava, Gulshan,Nguyen, Gia Nhu,Stanciu, Loredana-Mihaela
Publsiher: IGI Global
Total Pages: 321
Release: 2018-03-23
ISBN 10: 1522550984
ISBN 13: 9781522550983
Language: EN, FR, DE, ES & NL

Social Network Analytics for Contemporary Business Organizations Book Review:

Social technology is quickly becoming a vital tool in our personal, educational, and professional lives. Its use must be further examined in order to determine the role of social media technology in organizational settings to promote business development and growth. Social Network Analytics for Contemporary Business Organizations is a critical scholarly resource that analyzes the application of social media in business applications. Featuring coverage on a broad range of topics, such as business management, dynamic networks, and online interaction, this book is geared towards professionals, researchers, academics, students, managers, and practitioners actively involved in the business industry.

Sentiment Analysis in Social Networks

Sentiment Analysis in Social Networks
Author: Federico Alberto Pozzi,Elisabetta Fersini,Enza Messina,Bing Liu
Publsiher: Morgan Kaufmann
Total Pages: 284
Release: 2016-10-06
ISBN 10: 0128044381
ISBN 13: 9780128044384
Language: EN, FR, DE, ES & NL

Sentiment Analysis in Social Networks Book Review:

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics

Social Network Analysis for Startups

Social Network Analysis for Startups
Author: Maksim Tsvetovat,Alexander Kouznetsov
Publsiher: "O'Reilly Media, Inc."
Total Pages: 190
Release: 2011-10-06
ISBN 10: 1449306462
ISBN 13: 9781449306465
Language: EN, FR, DE, ES & NL

Social Network Analysis for Startups Book Review:

SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network). Thus, we may arrive at results that may seem counter-intuitive -- e.g. that Jusin Bieber (7.5 mil. followers) and Lady Gaga (7.2 mil. followers) have relatively little actual influence despite their celebrity status -- while a middle-of-the-road blogger with 30K followers is able to generate tweets that "go viral" and result in millions of impressions. O'Reilly's "Mining Social Media" and "Programming Collective Intelligence" books are an excellent start for people inteseted in SNA. This book builds on these books' foundations to teach a new, pragmatic, way of doing SNA. I would like to write a book that links theory ("why is this important?", "how do various concepts interact?", "how do I interpret quantitative results?") and practice -- gathering, analyzing and visualizing data using Python and other open-source tools.

Seven Layers of Social Media Analytics

Seven Layers of Social Media Analytics
Author: Gohar F. Khan
Publsiher: Createspace Independent Publishing Platform
Total Pages: 189
Release: 2015
ISBN 10: 9781507823200
ISBN 13: 1507823207
Language: EN, FR, DE, ES & NL

Seven Layers of Social Media Analytics Book Review:

The book offers concepts, tools, tutorials, and case studies that business managers need to extract and analyze the seven layers of social media data, including text, actions, networks, apps, hyperlinks, search engines, and location layers. Social media analytics is about converting unstructured social media data into meaningful business insights. By the end of this book, you will have mastered the concepts, techniques, and tools used to extract business insights from social media that help increase brand loyalty, generate leads, drive traffic, and ultimately make good business decisions. The book is non-technical in nature best suited for business managers, owners, consultants, students, and professors, etc. Here is how the book is structured: Chapter 1: The Seven Layers of Social Media Analytics Chapter 2: Understanding Social Media Chapter 3: Social Media Text Analytics Chapter 4: Social Media Network Analytics Chapter 5: Social Media Actions Analytics Chapter 6: Social Media Apps Analytics Chapter 7: Social Media Hyperlinks Analytics Chapter 8: Social Media Location Analytics Chapter 9: Social Media Search Engine Analytics Chapter 10: Aligning Social Media Analytics with Business Goals The book also comes with a companion site (http: //7layersanalytics.com/) which offers Updated Tutorials, Power-Point Slide, Case Studies, Sample Data, and Syllabus.

Exam Prep for: Social Network Analytics

Exam Prep for: Social Network Analytics
Author: Anonim
Publsiher: Anonim
Total Pages: 329
Release:
ISBN 10:
ISBN 13:
Language: EN, FR, DE, ES & NL

Exam Prep for: Social Network Analytics Book Review:

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques
Author: Bart Baesens,Wouter Verbeke,Veronique Van Vlasselaer
Publsiher: John Wiley & Sons
Total Pages: 400
Release: 2015-08-17
ISBN 10: 1119133122
ISBN 13: 9781119133124
Language: EN, FR, DE, ES & NL

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques Book Review:

Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post–implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti–money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

Python Social Media Analytics

Python Social Media Analytics
Author: Siddhartha Chatterjee,Michal Krystyanczuk
Publsiher: Packt Publishing Ltd
Total Pages: 312
Release: 2017-07-28
ISBN 10: 1787126757
ISBN 13: 9781787126756
Language: EN, FR, DE, ES & NL

Python Social Media Analytics Book Review:

Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more Analyze and extract actionable insights from your social data using various Python tools A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn Understand the basics of social media mining Use PyMongo to clean, store, and access data in MongoDB Understand user reactions and emotion detection on Facebook Perform Twitter sentiment analysis and entity recognition using Python Analyze video and campaign performance on YouTube Mine popular trends on GitHub and predict the next big technology Extract conversational topics on public internet forums Analyze user interests on Pinterest Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using the Python programming language. To explain various data analysis processes, real-world datasets are used wherever required.

Introduction to Social Network Analysis with R

Introduction to Social Network Analysis with R
Author: Michal Bojanowski
Publsiher: John Wiley & Sons
Total Pages: 350
Release: 2016-02-26
ISBN 10: 9781118456040
ISBN 13: 1118456041
Language: EN, FR, DE, ES & NL

Introduction to Social Network Analysis with R Book Review:

Introduction to Social Network Analysis with R provides an introduction to performing SNA studies using R, combining the theories of social networks and methods of social network analysis with the R environment as an open source system for statistical data analysis and graphics. Short introductions to both R and the topics of SNA are included, making the book accessible to those with little or no familiarity with either domain. The topics covered and the structure of the book mimic the stages of a typical SNA research project, and include chapters devoted to data importing, network data manipulation and selection, network visualisation and methods of de­scriptive SNA. Concepts of SNA are introduced and their application demonstrated with an extensive use of empirical examples which are based on a variety of real network datasets. Introduction to Social Network Analysis with R also provides background and theoretical motivations, which include examples of important theoretical models behind the presented methods. These numerous examples and case studies reveal how R can be used as a convenient simulation platform, and are accompanied by a supporting website featuring R functions and datasets used throughout the book.

Learning Social Media Analytics with R

Learning Social Media Analytics with R
Author: Raghav Bali,Dipanjan Sarkar,Tushar Sharma
Publsiher: Packt Publishing Ltd
Total Pages: 394
Release: 2017-05-26
ISBN 10: 1787125467
ISBN 13: 9781787125469
Language: EN, FR, DE, ES & NL

Learning Social Media Analytics with R Book Review:

Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

Mixed Methods Social Network Analysis

Mixed Methods Social Network Analysis
Author: Dominik E. Froehlich,Martin Rehm,Bart C. Rienties
Publsiher: Routledge
Total Pages: 280
Release: 2019-12-09
ISBN 10: 0429557043
ISBN 13: 9780429557040
Language: EN, FR, DE, ES & NL

Mixed Methods Social Network Analysis Book Review:

Mixed Methods Social Network Analysis brings together diverse perspectives from 42 international experts on how to design, implement, and evaluate mixed methods social network analysis (MMSNA). There is an increased recognition that social networks can be important catalysts for change and transformation. This edited book from leading experts in mixed methods and social network analysis describes how researchers can conceptualize, develop, mix, and intersect diverse approaches, concepts, and tools. In doing so, they can improve their understanding and insights into the complex change processes in social networks. Section 1 includes eight chapters that reflect on "Why should we do MMSNA?", providing a clear map of MMSNA research to date and why to consider MMSNA. In Section 2 the remaining 11 chapters are dedicated to the question "How do I do MMSNA?", illustrating how concentric circles, learning analytics, qualitative structured approaches, relational event modeling, and other approaches can empower researchers. This book shows that mixing qualitative and quantitative approaches to social network analysis can empower people to understand the complexities of change in networks and relations between people. It shows how mixed analysis can be applied to a wide range of data generated by diverse global communities: American school children, Belgian teachers, Dutch medical professionals, Finnish consultants, French school children, and Swedish right-wing social media users, amongst others. It will be of great interest to researchers and postgraduate students in education and social sciences and mixed methods scholars.

Creating Value with Social Media Analytics

Creating Value with Social Media Analytics
Author: Gohar F. Khan
Publsiher: Createspace Independent Publishing Platform
Total Pages: 510
Release: 2018-04-23
ISBN 10: 9781977543974
ISBN 13: 1977543979
Language: EN, FR, DE, ES & NL

Creating Value with Social Media Analytics Book Review:

Often termed as the ''new gold,'' the vast amount of social media data can be employed to identify which customer behavior and actions create more value. Nevertheless, many brands find it extremely hard to define what the value of social media is and how to capture and create value with social media data.In Creating Value with Social Media Analytics, we draw on developments in social media analytics theories and tools to develop a comprehensive social media value creation framework that allows readers to define, align, capture, and sustain value through social media data. The book offers concepts, strategies, tools, tutorials, and case studies that brands need to align, extract, and analyze a variety of social media data, including text, actions, networks, multimedia, apps, hyperlinks, search engines, and location data. By the end of this book, the readers will have mastered the theories, concepts, strategies, techniques, and tools necessary to extract business value from big social media that help increase brand loyalty, generate leads, drive traffic, and ultimately make sound business decisions. Here is how the book is organized. Chapter 1: Creating Value with Social Media Analytics Chapter 2: Understanding Social Media Chapter 3: Understanding Social Media Analytics Chapter 4: Analytics-Business Alignment Chapter 5: Capturing Value with Network Analytics Chapter 6: Capturing Value with Text Analytics Chapter 7: Capturing Value with Actions Analytics Chapter 8: Capturing Value with Search Engine Analytics Chapter 9: Capturing Value with Location Analytics Chapter 10: Capturing Value with Hyperlinks Analytics Chapter 11: Capturing Value with Mobile Analytics Chapter 12: Capturing Value with Multimedia Analytics Chapter 13: Social Media Analytics CapabilitiesChapter 14: Social Media Security, Privacy, & Ethics The book has a companion site (https://analytics-book.com/), which offers useful instructor resources. Praises for the book "Gohar F. Khan has a flair for simplifying the complexity of social media analytics. Creating Value with Social Media Analytics is a beautifully delineated roadmap to creating and capturing business value through social media. It provides the theories, tools, and creates a roadmap to leveraging social media data for business intelligence purposes. Real world analytics cases and tutorials combined with a comprehensive companion site make this an excellent textbook for both graduate and undergraduate students."-Robin Saunders, Director of the Communications and Information Management Graduate Programs, Bay Path University. "Creating Value with Social Media Analytics offers a comprehensive framework to define, align, capture, and sustain business value through social media data. The book is theoretically grounded and practical, making it an excellent resource for social media analytics courses."-Haya Ajjan, Director & Associate Prof., Elon Center for Organizational Analytics, Elon University. "Gohar Khan is a pioneer in the emerging domain of social media analytics. This latest text is a must-read for business leaders, managers, and academicians, as it provides a clear and concise understanding of business value creation with social media data from a social lens."-Laeeq Khan, Director, Social Media Analytics Research Team, Ohio University. "Whether you are coming from a business, research, science or art background, Creating Value with Social Media Analytics is a brilliant induction resource for those entering the social media analytics industry. The insightful case studies and carefully crafted tutorials are the perfect supplements to help digest the key concepts introduced in each chapter."-Jared Wong, Social Media Data Analyst, Digivizer "It is one of the most comprehensive books on analytics that I have come across recently."-Bobby Swar, Prof. Concordia Uni.

Social Network Analysis

Social Network Analysis
Author: Song Yang,Franziska B. Keller,Lu Zheng
Publsiher: SAGE Publications
Total Pages: 248
Release: 2016-10-28
ISBN 10: 1506362125
ISBN 13: 9781506362120
Language: EN, FR, DE, ES & NL

Social Network Analysis Book Review:

Social Network Analysis: Methods and Examples by Song Yang, Franziska B. Keller, and Lu Zheng prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.

Analyzing Social Media Networks with NodeXL

Analyzing Social Media Networks with NodeXL
Author: Derek Hansen,Ben Shneiderman,Marc A. Smith
Publsiher: Morgan Kaufmann
Total Pages: 304
Release: 2010-09-14
ISBN 10: 9780123822307
ISBN 13: 0123822300
Language: EN, FR, DE, ES & NL

Analyzing Social Media Networks with NodeXL Book Review:

Analyzing Social Media Networks with NodeXL offers backgrounds in information studies, computer science, and sociology. This book is divided into three parts: analyzing social media, NodeXL tutorial, and social-media network analysis case studies. Part I provides background in the history and concepts of social media and social networks. Also included here is social network analysis, which flows from measuring, to mapping, and modeling collections of connections. The next part focuses on the detailed operation of the free and open-source NodeXL extension of Microsoft Excel, which is used in all exercises throughout this book. In the final part, each chapter presents one form of social media, such as e-mail, Twitter, Facebook, Flickr, and Youtube. In addition, there are descriptions of each system, the nature of networks when people interact, and types of analysis for identifying people, documents, groups, and events. Walks you through NodeXL, while explaining the theory and development behind each step, providing takeaways that can apply to any SNA Demonstrates how visual analytics research can be applied to SNA tools for the mass market Includes case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and wikis Download companion materials and resources at https://nodexl.codeplex.com/documentation

Social Network Analysis

Social Network Analysis
Author: John Scott,John P. Scott
Publsiher: SAGE
Total Pages: 208
Release: 2000-03-25
ISBN 10: 9780761963394
ISBN 13: 0761963391
Language: EN, FR, DE, ES & NL

Social Network Analysis Book Review:

This edition is an accessible introduction to the theory and practice of network analysis in the social sciences. Scott outlines the theoretical basis of network analysis and the key techniques for using it as a research tool.

Social Media Analytics for User Behavior Modeling

Social Media Analytics for User Behavior Modeling
Author: Arun Reddy Nelakurthi,Jingrui He
Publsiher: CRC Press
Total Pages: 120
Release: 2020-01-29
ISBN 10: 1000025403
ISBN 13: 9781000025408
Language: EN, FR, DE, ES & NL

Social Media Analytics for User Behavior Modeling Book Review:

In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

Applications of Social Media and Social Network Analysis

Applications of Social Media and Social Network Analysis
Author: Przemysław Kazienko,Nitesh Chawla
Publsiher: Springer
Total Pages: 240
Release: 2015-05-28
ISBN 10: 3319190032
ISBN 13: 9783319190037
Language: EN, FR, DE, ES & NL

Applications of Social Media and Social Network Analysis Book Review:

This collection of contributed chapters demonstrates a wide range of applications within two overlapping research domains: social media analysis and social network analysis. Various methodologies were utilized in the twelve individual chapters including static, dynamic and real-time approaches to graph, textual and multimedia data analysis. The topics apply to reputation computation, emotion detection, topic evolution, rumor propagation, evaluation of textual opinions, friend ranking, analysis of public transportation networks, diffusion in dynamic networks, analysis of contributors to communities of open source software developers, biometric template generation as well as analysis of user behavior within heterogeneous environments of cultural educational centers. Addressing these challenging applications is what makes this edited volume of interest to researchers and students focused on social media and social network analysis.

Social Network Analysis

Social Network Analysis
Author: Stanley Wasserman,Stanley (University of Illinois Wasserman, Urbana-Champaign),Katherine Faust
Publsiher: Cambridge University Press
Total Pages: 825
Release: 1994-11-25
ISBN 10: 9780521387071
ISBN 13: 0521387078
Language: EN, FR, DE, ES & NL

Social Network Analysis Book Review:

Social network analysis is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. The social network perspective focuses on relationships among social entities and is an important addition to standard social and behavioral research, which is primarily concerned with attributes of the social units. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. It is a reference book that can be used by those who want a comprehensive review of network methods, or by researchers who have gathered network data and want to find the most appropriate method by which to analyze it. It is also intended for use as a textbook as it is the first book to provide comprehensive coverage of the methodology and applications of the field.