Social media is invaluable during crises like natural disasters, but difficult to analyze. This book shows how computer science can help.

A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Big data, surveillance, crisis management. Three largely different and richly researched fields, however, the interplay amongst these three domains is rarely addressed. Through unique international case studies this book examines the links between these three fields. Considering crisis management as an 'umbrella term' that covers a number of crises and ways of managing them, this book explores the collection of ‘big data’ by governmental crisis organisations, as well as the unintended consequences of using such data. In particular, through the lens of surveillance, the contributions investigate how the use and abuse of big data can easily lead to monitoring and controlling the behaviour of people affected by crises. Readers will understand that big data in crisis management must be examined as a political process, involving questions of power and transparency. A highly topical volume, Big Data, Surveillance and Crisis Management will appeal to postgraduate students and postdoctoral researchers interested in fields including Sociology and Surveillance Studies, Disaster and Crisis Management, Media Studies, Governmentality, Organisation Theory and Information Society Studies.

The overflow of information generated during disasters can be as paralyzing to humanitarian response as the lack of information. This flash flood of information‘social media, satellite imagery and more is often referred to as Big Data. Making sense of this data deluge during disasters is proving an impossible challenge for traditional humanitarian

This open access book explores a special species of trouble afflicting modern societies: creeping crises. These crises evolve over time, reveal themselves in different ways, and resist comprehensive responses despite periodic public attention. As a result, these crises continue to creep in front of our eyes. This book begins by defining the concept of a creeping crisis, showing how existing literature fails to properly define and explore this phenomenon and outlining the challenges such crises pose to practitioners. Drawing on ongoing research, this book presents a diverse set of case studies on: antimicrobial resistance, climate change-induced migration, energy extraction, big data, Covid-19, migration, foreign fighters, and cyberattacks. Each chapter explores how creeping crises come into existence, why they can develop unimpeded, and the consequences they bring in terms of damage and legitimacy loss. The book provides a proof-of-concept to help launch the systematic study of creeping crises. Our analysis helps academics understand a new species of threat and practitioners recognize and prepare for creeping crises.

Defense of Scientific Hypothesis: From Reproducibility Crisis to Big Data argues that the scientific hypothesis is the key to understanding what science is about, and explains its importance for scientists and non-scientists alike. Most scientists, like the general public, receive only cursory formal instruction about the scientific hypothesis. Since we all constantly assess what's going on around us, we continually formulate and test hypotheses, consciously and unconsciously. The book distinguishes scientific from statistical hypotheses, analyzes the benefits of hypotheses and hypothesis testing, sorts out sciences that do not require hypotheses, discusses educational and social policies relating to the hypothesis, and offers advice on recognizing and formulating hypotheses.

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 the 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.