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The U.S. no longer has a free marketplace of ideas. Instead, the marketplace is saturated with covert foreign-backed disinformation. And despite the ethical obligations to act, successive administrations have done nothing. Additionally, the decline in trust has left the door open for populism and illiberalism to enter. Some believe the very fabric of American liberalism is at stake. So what are the ethical responsibilities of the executive branch to counter covert campaigns such as the one coming from Russian-backed disinformation circulating within the US? Why has the government failed to act? So far, the practical challenges are daunting if the executive branch addresses the threat to the homeland. The process to limit this problem is wrought with profound political implications. By its very nature, social media-based disinformation is inextricably linked with existing complex societal cleavages, the First Amendment, and politics. But the failure to do anything is a serious abdication of the government¿s ethical responsibilities. This raises the question of where the line is for government intervention. This work provides answers.

This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications.

This book examines modern paradigms of disease control based on social network surveillance applications, including electronic sentinel surveillance and wireless application-based surveillance science. It also highlights topics that integrate statistical and epidemiological sciences with surveillance practice and, in order to reflect the evolution of social networking practices, discusses topics concerning the challenges for surveillance theory and practice.In turn, the book goes a step further by providing insights on how we need to analyse epidemiological trends by following best practices on distinguishing useful information from noise, namely fake news, false reporting of disease incidents and events, etc. At the same time, we need to be able to protect health-focused applications and communication tools via cybersecurity technologies and to ensure that anonymity of reporting and privacy are preserved. In closing, the book discusses the role and impact of social media on disease surveillance, as well as the current role of communities in infectious disease surveillance and control.

This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains.

This book focusses on recommendation, behavior, and anomaly, among of social media analysis.

This book is related to the educational networking (EN) domain, an incipient but disrupting trend engaged in extending and improving formal and informal academic practices by means of the support given by online social networks (OSNs) and Web 2.0 technologies. With the aim of contributing to spread the knowledge and development of the arena, this volume introduces ten recent works, whose content meets the quality criteria of formal scientific labor that is worthy to be published according to following five categories:· Reviews: gather three overviews that focus on K-12 EN practice, mixed methods approaches using social network analysis for learning and education, and a broad landscape of the recent accomplished labor.· Conceptual: presents a work where a theoretical framework is proposed to overcome barriers that constrain the use of OSNs for educational purposes bymeans of a Platform Adoption Model. · Projects: inform a couple of initiatives, where one fosters groups and networks for teachers involved in distance education, and the other encourages students the author academic videos to improve motivation and engagement.· Approaches: offer three experiences related to: Wiki and Blog usage for assessment affairs, application of a method that encourages OSNs users to actively post and repost valuable information for the learning community, and the recreation of learning spaces in context¿aware to boost EN.· Study: applies an own method to ranking Mexican universities based on maximal clique, giving as a result a series of complex visual networks that characterize the tides among diverse features that describe academic institutions practice.In resume, this volumeoffers a fresh reference of an emergent field that contributes to spreading and enhancing the provision of education in classrooms and online settings through social constructivism and collaboration policy. Thus, it is expected the published content encourages researchers, practitioners, professors, and postgraduate students to consider their future contribution to extent the scope and impact of EN in formal and informal teaching and learning endeavors.

Some authors explore new trends related to network measures, multilevel networks and clustering on networks, while other contributions deepen the relationship among statistical methods for data mining and social network analysis.

This book shows how open source intelligence can be a powerful tool for combating crime by linking local and global patterns to help understand how criminal activities are connected. Readers will encounter the latest advances in cutting-edge data mining, machine learning and predictive analytics combined with natural language processing and social network analysis to detect, disrupt, and neutralize cyber and physical threats. Chapters contain state-of-the-art social media analytics and open source intelligence research trends. This multidisciplinary volume will appeal to students, researchers, and professionals working in the fields of open source intelligence, cyber crime and social network analytics. Chapter Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains.

This book focuses on applications of social network analysis in predictive policing. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature.

Through a detailed critique of existing online social networks, this volume presents the technical and methodological aspects of designing social media platforms and applications. Readers will learn current techniques and best practices for designing and implementing social networks on the web.

This new interdisciplinary work presents system dynamics as a powerful approach to enable analysts build simulation models of social systems, with a view toward enhancing decision making.

This book focusses on recommendation, behavior, and anomaly, among of social media analysis.

Some authors explore new trends related to network measures, multilevel networks and clustering on networks, while other contributions deepen the relationship among statistical methods for data mining and social network analysis.

This book is related to the educational networking (EN) domain, an incipient but disrupting trend engaged in extending and improving formal and informal academic practices by means of the support given by online social networks (OSNs) and Web 2.0 technologies. With the aim of contributing to spread the knowledge and development of the arena, this volume introduces ten recent works, whose content meets the quality criteria of formal scientific labor that is worthy to be published according to following five categories:┬╖         Reviews: gather three overviews that focus on K-12 EN practice, mixed methods approaches using social network analysis for learning and education, and a broad landscape of the recent accomplished labor.┬╖         Conceptual: presents a work where a theoretical framework is proposed to overcome barriers that constrain the use of OSNs for educational purposes by means of a Platform Adoption Model. ┬╖         Projects: inform a couple of initiatives, where one fosters groups and networks for teachers involved in distance education, and the other encourages students the author academic videos to improve motivation and engagement.┬╖         Approaches: offer three experiences related to: Wiki and Blog usage for assessment affairs, application of a method that encourages OSNs users to actively post and repost valuable information for the learning community, and the recreation of learning spaces in contextΓÇôaware to boost EN.┬╖         Study: applies an own method to ranking Mexican universities based on maximal clique, giving as a result a series of complex visual networks that characterize the tides among diverse features that describe academic institutions practice.In resume, this volume offers a fresh reference of an emergent field that contributes to spreading and enhancing the provision of education in classrooms and online settings through social constructivism and collaboration policy. Thus, it is expected the published content encourages researchers, practitioners, professors, and postgraduate students to consider their future contribution to extent the scope and impact of EN in formal and informal teaching and learning endeavors.

This book is a timely collection of chapters that present the state of the art within the analysis and application of big data.

This book addresses and examines the impacts of applications and services for data management and analysis, such as infrastructure, platforms, software, and business processes, on both academia and industry.

This book focuses on recent technical advancements and state-of-the art technologies for analyzing characteristic features and probabilistic modelling of complex social networks and decentralized online network architectures.

This edited volume presents advances in modeling and computational analysis techniques related to networks and online communities. This book will appeal to students, professors, and researchers working in the fields of data science, computational social science, and social network analysis.

The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking.

This book addresses the challenges of social network and social media analysis in terms of prediction and inference. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection.

This book presents the state-of-the-art in various aspects of analysis and mining of online social networks.

The contributors in this book share, exchange, and develop new concepts, ideas, principles, and methodologies in order to advance and deepen our understanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making.

In addition, a Social Networking and Education Model (SNEM) will be developed to promote and implement Social Networking in the education sector.

Important aspects of social networking analysis are covered in this work by combining experimental and theoretical research.