Generative Methods For Social Media Analysis
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| Author | : Stan Matwin |
| Publisher | : Springer Nature |
| Total Pages | : 92 |
| Release | : 2023-07-05 |
| Genre | : Mathematics |
| ISBN | : 3031336178 |
This book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks. The book is divided into five chapters and provides an extensive bibliography consisting of more than 250 papers. After a quick introduction and survey of the book in the first chapter, chapter 2 is devoted to the discussion of data models and ontologies for social network analysis. Next, chapter 3 deals with text generation and generative text models and the dangers they pose to social media and society at large. Chapter 4 then focuses on topic modelling and sentiment analysis in the context of social networks. Finally, Chapter 5 presents graph theory tools and approaches to mine and model social networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified. The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications.
| Author | : Xu, Guandong |
| Publisher | : IGI Global |
| Total Pages | : 272 |
| Release | : 2013-01-31 |
| Genre | : Computers |
| ISBN | : 1466628073 |
Social Media Mining and Social Network Analysis: Emerging Research highlights the advancements made in social network analysis and social web mining and its influence in the fields of computer science, information systems, sociology, organization science discipline and much more. This collection of perspectives on developmental practice is useful for industrial practitioners as well as researchers and scholars.
| Author | : Klaus Bredl |
| Publisher | : Routledge |
| Total Pages | : 202 |
| Release | : 2017-07-05 |
| Genre | : Social Science |
| ISBN | : 1351558404 |
Social media is becoming increasingly attractive for users. It is a fast way to communicate ideas and a key source of information. It is therefore one of the most influential mediums of communication of our time and an important area for audience research. The growth of social media invites many new questions such as: How can we analyze social media? Can we use traditional audience research methods and apply them to online content? Which new research strategies have been developed? Which ethical research issues and controversies do we have to pay attention to? This book focuses on research strategies and methods for analyzing social media and will be of interest to researchers and practitioners using social media, as well as those wanting to keep up to date with the subject. This book was originally published as a special issue of the Journal of Technology in Human Services.
| Author | : Tansel Özyer |
| Publisher | : Springer |
| Total Pages | : 241 |
| Release | : 2018-05-30 |
| Genre | : Social Science |
| ISBN | : 3319899325 |
The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.
| Author | : Luke Sloan |
| Publisher | : SAGE |
| Total Pages | : 709 |
| Release | : 2017-01-26 |
| Genre | : Social Science |
| ISBN | : 1473987210 |
With coverage of the entire research process in social media, data collection and analysis on specific platforms, and innovative developments in the field, this handbook is the ultimate resource for those looking to tackle the challenges that come with doing research in this sphere.
| Author | : Subodha Kumar |
| Publisher | : CRC Press |
| Total Pages | : 68 |
| Release | : 2021-12-30 |
| Genre | : Technology & Engineering |
| ISBN | : 1000515338 |
Social Media Analytics and Practical Applications: The Change to the Competition Landscape provides a framework that allows you to understand and analyze the impact of social media in various industries. It illustrates how social media analytics can help firms build transformational strategies and cope with the challenges of social media technology. By focusing on the relationship between social media and other technology models, such as wisdom of crowds, healthcare, fintech and blockchain, machine learning methods, and 5G, this book is able to provide applications used to understand and analyze the impact of social media. Various industries are called out and illustrate how social media analytics can help firms build transformational strategies and at the same time cope with the challenges that are part of the landscape. The book discusses how social media is a driving force in shaping consumer behavior and spurring innovations by embracing and directly engaging with consumers on social media platforms. By closely reflecting on emerging practices, the book shows how to take advantage of recent advancements and how business operations are being revolutionized. Social Media Analytics and Practical Applications is written for academicians and professionals involved in social media and social media analytics.
| Author | : He-Yan Huang |
| Publisher | : Springer |
| Total Pages | : 267 |
| Release | : 2014-10-21 |
| Genre | : Computers |
| ISBN | : 3662455587 |
This book constitutes the thoroughly refereed papers of the Third National Conference of Social Media Processing, SMP 2014, held in Beijing, China, in November 2014. The 14 revised full papers and 9 short papers presented were carefully reviewed and selected from 101 submissions. The papers focus on the following topics: mining social media and applications; natural language processing; data mining; information retrieval; emergent social media processing problems.
| Author | : Kun Yue |
| Publisher | : |
| Total Pages | : 290 |
| Release | : 2020 |
| Genre | : Content analysis (Communication) |
| ISBN | : 9811207380 |
"This unique compendium focuses on the acquisition and analysis of social media data. The approaches concern both the data-intensive characteristics and graphical structures of social media. The book addresses the critical problems in social media analysis, which representatively cover its lifecycle. The must-have volume is an excellent reference text for professionals, researchers, academics and graduate students in AI and databases"--
| Author | : Donghyuk Shin |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model's power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.
| Author | : Erik Cambria |
| Publisher | : Springer |
| Total Pages | : 199 |
| Release | : 2017-04-07 |
| Genre | : Medical |
| ISBN | : 3319553941 |
Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers’ sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well.
