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Deviance in social media and social cyber forensics : uncovering hidden relations using open source information (OSINF)
This book describes the methodologies and tools used to conduct social cyber forensic analysis. By applying these methodologies and tools on various events observed in the case studies contained within, their effectiveness is highlighted. They blend computational social network analysis and cyber forensic concepts and tools in order to identify and study information competitors. Through cyber forensic analysis, metadata associated with propaganda-riddled websites are extracted. This metadata assists in extracting social network information such as friends and followers along with communication network information such as networks depicting flows of information among the actors such as tweets, replies, retweets, mentions, and hyperlinks. Through computational social network analysis, the authors identify influential actors and powerful groups coordinating the disinformation campaign. A blended social cyber forensic approach allows them to study cross-media affiliations of the information competitors. For instance, narratives are framed on blogs and YouTube videos, and then Twitter and Reddit, for instance, will be used to disseminate the message. Social cyber forensic methodologies enable researchers to study the role of modern information and communication technologies (ICTs) in the evolution of information campaign and coordination. In addition to the concepts and methodologies pertaining to social cyber forensics, this book also offers a collection of resources for readers including several datasets that were collected during case studies, up-to-date reference and literature surveys in the domain, and a suite of tools that students, researchers, and practitioners alike can utilize. Most importantly, the book demands a dialogue between information science researchers, public affairs officers, and policy makers to prepare our society to deal with the lawless \"wild west\" of modern social information systems triggering debates and studies on cyber diplomacy.
Book
Connected Code
Coding, once considered an arcane craft practiced by solitary techies, is now recognized by educators and theorists as a crucial skill, even a new literacy, for all children. Programming is often promoted in K-12 schools as a way to encourage \"computational thinking\" -- which has now become the umbrella term for understanding what computer science has to contribute to reasoning and communicating in an ever-increasingly digital world.InConnected Code,Yasmin Kafai and Quinn Burke argue that although computational thinking represents an excellent starting point, the broader conception of \"computational participation\" better captures the twenty-first-century reality. Computational participation moves beyond the individual to focus on wider social networks and a DIY culture of digital \"making.\" Kafai and Burke describe contemporary examples of computational participation: students who code not for the sake of coding but to create games, stories, and animations to share; the emergence of youth programming communities; the practices and ethical challenges of remixing (rather than starting from scratch); and the move beyond stationary screens to programmable toys, tools, and textiles.
eBook
Big Data, Little Data, No Data
\"Big Data\" is on the covers ofScience, Nature, theEconomist, andWiredmagazines, on the front pages of theWall Street Journaland theNew York Times.But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines.Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six \"provocations\" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
eBook
Research methods in human-computer interaction
Research Methods in Human-Computer Interaction is a comprehensive guide to performing research and is essential reading for both quantitative and qualitative methods.Since the first edition was published in 2009, the book has been adopted for use at leading universities around the world, including Harvard University, Carnegie-Mellon University.
eBook
Hunting the cyber trail : be a computer forensic scientist
Forensic science is often associated with dead bodies, but forensics the use of science to solve crimes is increasingly needed in the modern world to crack cybercrimes. Computer forensic investigators locate and analyze data that can lead to the arrest of cybercriminals, such as those who plant malicious code and steal personal information. Future forensic scientists will love learning how professionals chase down criminals in the cyber world through a thrilling case in which essential aspects of this career are applied in true-to-life situations. Realistic photographs and images make this volume both gripping and educational.
Book
Data Clustering
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process-including how to verify the quality of the underlying clusters-through supervision, human intervention, or the automated generation of alternative clusters.
eBook
SciPy 1.0: fundamental algorithms for scientific computing in Python
SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
This Perspective describes the development and capabilities of SciPy 1.0, an open source scientific computing library for the Python programming language.
Journal Article