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 4, 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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Time is a precious commodity, especially if you're a system administrator. No other job pulls people in so many directions at once. Users interrupt you constantly with requests, preventing you from getting anything done. Your managers want you to get long-term projects done but flood you with requests for quick-fixes that prevent you from ever getting to those long-term projects. But the pressure is on you to produce and it only increases with time. What do you do?

The answer is time management. And not just any time management theory--you want Time Management for System Administrators, to be exact. With keen insights into the challenges you face as a sys admin, bestselling author Thomas Limoncelli has put together a collection of tips and techniques that will help you cultivate the time management skills you need to flourish as a system administrator.

Time Management for System Administrators understands that an Sys Admin often has competing goals: the concurrent responsibilities of working on large projects and taking care of a user's needs. That's why it focuses on strategies that help you work through daily tasks, yet still allow you to handle critical situations that inevitably arise.

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Manage interruptionsEliminate timewastersKeep an effective calendarDevelop routines for things that occur regularlyUse your brain only for what you're currently working onPrioritize based on customer expectationsDocument and automate processes for faster execution

What's more, the book doesn't confine itself to just the work environment, either. It also offers tips on how to apply these time management tools to your social life. It's the first step to a more productive, happier you.

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.
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