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Refining big data is a new multipurpose way to find, collect, and analyze information obtained from the web and off-line information sources about any research subject. It gives the opportunity to investigate (with an assumed level of statistical significance) the past and current status of information on a subject, and it can even predict the future. The refining of big data makes it possible to quantitatively investigate a wide spectrum of raw information on significant human issues—social, scientific, political, business, and others. Refining creates a space for new, rich sources of information and opens innovative ways for research. The article describes a procedure for refining big data and gives examples of its use.

Chinese social media and big data represent an important share of the global Internet, but have received relatively less attention. This editorial examines three dominant discourses based on China's distinctive and complex political, economic and social realities: "Big Data" (technical focus), "Big Brother" (political focus), and "Big Profit" (economic focus). We argue that the prevailing discourse and practice of big data in China is largely technocentric, decontextualized and nonreflexive, and much less attuned to the social, political, cultural, epistemological, and ethical implications of big data that a humancentric approach would demand. Second, the authoritarian Chinese state poses incredible political challenges to big data research and practice. Third, the practice of Chinese social media and big data is imbued with a discourse of technological nationalism, driven by a handful of monopolistic "national champions." Despite contention, the state and market players have formed a largely mutually beneficial symbiotic relationship to maximize their political and economic gains. We argue a comparative perspective to foster a global conversation on social media and big data is necessary in order to formulate collective responses to such challenges.

Digitalization, Big Data and fantasies in education One may be surprised what digitalization and Big Data are being used for in education. Through digital technologies, Big Data is being gathered to provide access for politicians and the public to school matters in general and specifically if schools and teachers do not reach determined goals. For example, attainment of socioeconomic reference indicators, measured by students' grade point average. Many fantasies are attached to digitalization and Big Data. Fantasies about increased transparency, safety, and prediction (Zuboff, 2019). However, digitalization and Big Data may also produce a culture of 'shaming and blaming', displaying those schools that do not live up to fixed goals. This in turn may lead to goal-fixation, nearsightedness, and 'prophylactic' reasoning in schools. In this paper we analyze the fantasies and powerful beliefs that make it difficult to problematize and critically reflect on the emergence of digitalization and Big Data. Inspired by psychoanalytical theory (Zizek, 2008), political and organizational theory (Ball, 2008; Agamben, 2013; Weick et al. 2005; Gioia & Chittipeddi, 1991), and anthropological studies of the performance of magic in 'primitive' and 'modern' organizations (Malinowski, 1948; Clark and Salaman, 1996), we will discuss examples of fantasies from previous and ongoing research (Rüsselbæk Hansen & Phelan, 2019); Bøje et al., 2018; Balslev & Raae 2019). We argue that delicate balances between monitoring and democracy are tipping. Furthermore, we argue that digitalization and Big Data support and are being supported by a neoliberal fantasy where measurement, clarity, comparison, and competition set the educational scene (Brown, 2015). That way a certain regime is produced which seems to regulate schools, teachers and students in ways that risk dismantling democratic engagement and conversation. This scenario will be illustrated as well as discussed: how can this be avoided so democracy does not turn into another lost ideal in education?

Wenn die Digitalisierung vor Schulen keinen Halt macht, so werden natürlich auch an dieser Stelle Daten generiert. Diese können anschließend genutzt werden, um individuelle oder auch gruppenspezifische Unterschiede zu erkennen und damit Lernprozesse effektiver zu gestalten.

So kann bei Online-Lernprogrammen festgestellt werden, wer sich wie lange mit bestimmten Inhalten beschäftigt und wie häufig er bestimmte Dinge frequentiert. Daraus können Rückschlüsse auf das Programm selbst gezogen werden, wodurch sich wiederum Möglichkeiten zur Verbesserung desselben auftun, wird Prof. Christof Meinel in der Süddeutschen Zeitung zitiert.

Ich könnte mir durchaus vorstellen, dass in einem Unterrichtskonzept, welches digitale und analoge Angebote gleichermaßen nutzt, Daten sehr genaue Rückschlüsse auf Verständnisprobleme, die sich aus dem traditionellen Unterricht ergeben, geben können. Ein solches, zeitlich möglichst unmittelbares Feedback an die Lehrkraft könnte diese etwa zu wichtigen Umstrukturierungen von Unterrichtseinheiten bewegen.

In eine ähnliche Richtung zielt die Anregung von Viktor Mayer-Schönberger, Schüler*innen könnten Literatur auf Tablet-Computern lesen und markieren und der Lehrkraft damit wichtige Informationen über Verständnisprobleme oder besonders interessante Stellen schon vor Beginn der Unterrichtsstunde zukommen lassen.

Neben den direkten, potentiell unterrichtsverbessernden Möglichkeiten, die Big Data uns eröffnet, sehe ich aber vor allem auch die Chance, durch die Nutzung ebendieser mit den Schüler*innen einen Dialog über Datenschutz zu eröffnen, da auf diese Weise immer wieder sichtbar wird, wie Daten generiert, ausgewertet und genutzt werden.

In diesem Kontext können und müssen dann Chancen und Risiken der verwendeten Technologien immer wieder angesprochen und diskutiert werden. Einen besseren Weg, dieses Thema nachhaltig in den Köpfen der Lernenden zu verankern kann es daher kaum geben. Wenn die Chance denn genutzt wird - ein bloßes Verwenden von Datenanalysen hätte vermutlich eher einen gegenteiligen Effekt.

2.5 quintillion bytes of data are created every day through pictures, messages, gps-data, etc. "Big Data" is seen simultaneously as the new Philosophers Stone and Pandora's box: a source of great knowledge and power, but equally, the root of serious problems.

AbstractThe newly established judicial-transparency platforms, like China Judgements Online, have provided access to a new resource—judicial big data—making it possible to conduct empirical, big-data-based legal research. However, as is often the case with new products, these platforms—China Judgements Online, in particular—pose a few problems for big-data-based legal research: insufficient academic depth; immature technical methods; and lack of innovation due to flawed data, strict technical thresholds, and lack of theoretical ambition and ability. In the future, big-data-based legal research should make use of current data resources, continue to promote statistical science and computer science in research, and apply small-data research methods, and in the meanwhile pay attention to the combination of data and theory.

This book focuses on the basic concepts and the related technologies of data mining for social medial. Topics include: big data and social data, data mining for making a hypothesis, multivariate analysis for verifying the hypothesis, web mining and media mining, natural language processing, social big data applications, and scalability. It explains analytical techniques such as modeling, data mining, and multivariate analysis for social big data. This book is different from other similar books in that presents the overall picture of social big data from fundamental concepts to applications whi

Intro -- Preface -- Overview of the Book -- Intended Audience -- Prerequisites -- Contents -- Contributors -- Glossary -- Introduction to Emergency Management -- 1 What Is Emergency? -- 2 Emergency Management -- 3 Emergency Management in Social Media Age: Information Flows -- 4 Emergency Management using Big Data -- 5 Tasks in Data-Driven Emergency Management -- References -- Big Data -- 1 What Is Big Data? -- 2 Big Data Sources for Emergency Management -- 3 Big Data Benefits and Challenges -- 3.1 Benefits -- 3.2 Challenges -- 4 Big Data Techniques and Tools -- 5 General Engine for Big Data Processing: Spark -- 6 Ethical and Societal Issues -- Exercises -- References -- Learning Algorithms for Emergency Management -- 1 Machine Learning and Emergency Management -- 1.1 Preliminaries -- 1.2 Learning Algorithms and Its Usage -- 1.2.1 Decision Tree -- 1.2.2 Clustering -- 1.2.3 Support Vector Machine -- 1.2.4 Bayesian -- 1.2.5 Neural Networks -- 1.2.6 Deep Learning -- 2 Practices of Learning Techniques in Emergency Management -- 2.1 Data Sets -- 2.2 Decision Trees in R -- 2.3 Naïve Bayes in R -- 2.4 k-Means Clustering in R -- 2.5 Support Vector Machine in R -- 2.6 Artificial Neural Networks in R -- References -- Knowledge Graphs and Natural-Language Processing -- 1 What Are Knowledge Graphs? -- 2 Benefits and Challenges -- 2.1 Benefits -- 2.2 Challenges -- 3 Vocabularies for Emergency Response -- 4 Semantic Datasets for Emergency Management -- 5 Analysing Natural-Language Texts -- 5.1 Pre-processing -- 5.2 Word Embeddings -- 5.3 Analysis Problems -- 5.4 Discussion -- 6 Using a Sentiment Analyser -- Exercises -- References -- Social Media Mining for Disaster Management and Community Resilience -- 1 Social Media and Disasters -- 2 Scenarios of Using Social Media Mining -- 2.1 Filtering Social Data for Actionable Intelligence.

Any peace process is an exercise in the negotiation of big data. From centuries old communal hagiography to the reams of official texts, media coverage and social media updates, peace negotiations generate data. Peacebuilding and peacekeeping today are informed by, often respond and contribute to big data. This is no easy task. As recently as a few years ago, before the term big data embraced the virtual on the web, what informed peace process design and implementation was in the physical domain – from contested borders and resources to background information in the form of text. The move from analogue, face-to-face negotiations to online, asynchronous, web-mediated negotiations – which can still include real world meetings – has profound implications for how peace is strengthened in fragile democracies.

The purpose of the article is to examine the rationale behind the argument of the geopolitical nature of Big Data, associated with Artificial Intelligence (AI). To this end, it advances in the classification of different dimensions that extend its understanding from its methodological, cognitive, ideological, geopolitical and practical function, as well as in the study of the factors that determine its geopolitical nature. This leads to the qualitative transformations that characterize the content and impact of geopolitical competition in the information society. The central conclusion is that the technological advances that express the geopolitical nature of these tools are modifying power relations and the way in which States, and these with their territories, relate to each other, thus revolutionizing traditional notions and approaches to understanding geopolitics in the context of the information society. In this sense, national interests and their strategic lines are being rethought, as well as the projection of their power in the political geography of nations on an international scale. For the development of the research, evaluations were applied from the qualitative paradigm, applying the method of triangulation of authors, which allowed the identification of the elements that distinguish the debate about the geopolitical nature of the united Big Data and Artificial Intelligence. ; El propósito del artículo consiste en examinar los fundamentos que sirven de base para la argumentación de la naturaleza geopolítica del Big Data, asociado a la Inteligencia Artificial (IA). Para ello se avanza en la clasificación de diferentes dimensiones que amplían su comprensión desde su función metodológica, cognoscitiva, ideológica, geopolítica y práctica, así como en el estudio de los factores que determinan su naturaleza geopolítica. De ello se inducen las transformaciones cualitativas de que caracterizan el contenido e impacto de la competencia geopolítica de la sociedad de la información. La conclusión central es que los avances tecnológicos que expresan naturaleza geopolítica de estas herramientas, están modificando las relaciones de poder y la manera en que se relacionan los Estados, y estos con sus territorios, con lo que se están revolucionando las nociones y enfoques tradicionales de entender la geopolítica en el contexto de la sociedad de la información. En este sentido, se están repensando los intereses nacionales y sus líneas estratégicas, así como, la proyección de su poder en la geografia política de las naciones a escala internacional. Para el desarrollo de la investigación se aplicaron valoraciones desde el paradigma cualitativo, aplicando el método de triangulación de autores, que permitió identificar los elementos que distinguen el debate acerca de la naturaleza geopolítica del Big Data unido y a la Inteligencia Artificial.