Big Crisis Data: Social Media in Disasters and Time-Critical Situations

Cambridge University Press
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Social media is an invaluable source of time-critical information during a crisis. However, emergency response and humanitarian relief organizations that would like to use this information struggle with an avalanche of social media messages that exceeds human capacity to process. Emergency managers, decision makers, and affected communities can make sense of social media through a combination of machine computation and human compassion - expressed by thousands of digital volunteers who publish, process, and summarize potentially life-saving information. This book brings together computational methods from many disciplines: natural language processing, semantic technologies, data mining, machine learning, network analysis, human-computer interaction, and information visualization, focusing on methods that are commonly used for processing social media messages under time-critical constraints, and offering more than 500 references to in-depth information.
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About the author

Carlos Castillo is a researcher in social computing. He is a web miner with a background in information retrieval, and has been influential in the areas of web content quality and credibility. He has co-authored more than seventy publications in top-tier international conferences and journals, a monograph on adversarial web search, and a book on information and influence propagation.

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Additional Information

Publisher
Cambridge University Press
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Published on
Jul 14, 2016
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Pages
225
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ISBN
9781316694572
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Language
English
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Genres
Computers / Computer Science
Computers / Social Aspects / General
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Content Protection
This content is DRM protected.
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Wei Chen
Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.
Oscar Gabriel Villegas Torres
Debido al rápido crecimiento demográfico, la producción de alimentos debe ser considerada una prioridad. Toma importancia la innovación de técnicas que permiten un mayor control de los factores del ambiente orientados a la producción intensiva.

En este sentido, el uso de sustratos obliga a no descuidar la correcta utilización de los recursos naturales. La producción de plántulas requiere de grandes cantidades de sustratos que en su mayoría son orgánicos. Este uso ya generalizado fomenta la dependencia tecnológica y el aumento de los costos de producción lo que ha comenzado a restringir su uso. Actualmente existe una tendencia creciente hacia la búsqueda de materiales que puedan sustituirla, por el alto costo de la turba de calidad, y sobre todo por la consideración medioambiental de las turberas en Europa.

La caracterización física de sustratos mediante el método propuesto por la Universidad de Florida (Dilger, 1998) y modificado por Pire y Pereira (2003) empleando el uso de porómetros, se considera confiable. El proceso de optimización es útil para determinar los porcentajes de fibra de coco y vermiculita para las diferentes mezclas, siendo la granulometría esencial por su influencia directa en las características físicas. De las once mezclas resultantes del proceso de optimización sólo se evaluaron tres, debido a su densidad. Todas las características físicas de los tres sustratos estuvieron en el rango considerado como deseable. El polvo de coco es un material de origen natural con potencial para sustituir a la turba en los sustratos destinados para la producción de plántulas en contenedor. @page { margin: 2cm } p { margin-bottom: 0.25cm; line-height: 120% } p.western { font-family: "Arial", sans-serif; font-size: 13pt } p.cjk { font-size: 10pt }
Carlos Castillo-Chavez
This IMA Volume in Mathematics and its Applications MATHEMATICAL APPROACHES FOR EMERGING AND REEMERGING INFECTIOUS DISEASES: MODELS, AND THEORY METHODS is based on the proceedings of a successful one week workshop. The pro ceedings of the two-day tutorial which preceded the workshop "Introduction to Epidemiology and Immunology" appears as IMA Volume 125: Math ematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction. The tutorial and the workshop are integral parts of the September 1998 to June 1999 IMA program on "MATHEMATICS IN BI OLOGY. " I would like to thank Carlos Castillo-Chavez (Director of the Math ematical and Theoretical Biology Institute and a member of the Depart ments of Biometrics, Statistics and Theoretical and Applied Mechanics, Cornell University), Sally M. Blower (Biomathematics, UCLA School of Medicine), Pauline van den Driessche (Mathematics and Statistics, Uni versity of Victoria), and Denise Kirschner (Microbiology and Immunology, University of Michigan Medical School) for their superb roles as organizers of the meetings and editors of the proceedings. Carlos Castillo-Chavez, es pecially, made a major contribution by spearheading the editing process. I am also grateful to Kenneth L. Cooke (Mathematics, Pomona College), for being one of the workshop organizers and to Abdul-Aziz Yakubu (Mathe matics, Howard University) for serving as co-editor of the proceedings. I thank Simon A. Levin (Ecology and Evolutionary Biology, Princeton Uni versity) for providing an introduction.
Daniel Zeng
This book on Infectious Disease Informatics (IDI) and biosurveillance is intended to provide an integrated view of the current state of the art, identify technical and policy challenges and opportunities, and promote cross-disciplinary research that takes advantage of novel methodology and what we have learned from innovative applications. This book also fills a systemic gap in the literature by emphasizing informatics driven perspectives (e.g., information system design, data standards, computational aspects of biosurveillance algorithms, and system evaluation). Finally, this book attempts to reach policy makers and practitioners through the clear and effective communication of recent research findings in the context of case studies in IDI and biosurveillance, providing “hands-on” in-depth opportunities to practitioners to increase their understanding of value, applicability, and limitations of technical solutions. This book collects the state of the art research and modern perspectives of distinguished individuals and research groups on cutting-edge IDI technical and policy research and its application in biosurveillance. The contributed chapters are grouped into three units. Unit I provides an overview of recent biosurveillance research while highlighting the relevant legal and policy structures in the context of IDI and biosurveillance ongoing activities. It also identifies IDI data sources while addressing information collection, sharing, and dissemination issues as well as ethical considerations. Unit II contains survey chapters on the types of surveillance methods used to analyze IDI data in the context of public health and bioterrorism. Specific computational techniques covered include: text mining, time series analysis, multiple data streams methods, ensembles of surveillance methods, spatial analysis and visualization, social network analysis, and agent-based simulation. Unit III examines IT and decision support for public health event response and bio-defense. Practical lessons learned in developing public health and biosurveillance systems, technology adoption, and syndromic surveillance for large events are discussed. The goal of this book is to provide an understandable interdisciplinary IDI and biosurveillance reference either used as a standalone textbook or reference for students, researchers, and practitioners in public health, veterinary medicine, biostatistics, information systems, computer science, and public administration and policy.
Gerardo Chowell
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