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Human-computer systems that treat context simply as enumerated facts, rules, or axioms about the surrounding physical and social environment will always have trouble handling information requiring human pragmatic interpretation. One way to overcome such limitations is to draw upon human pragmatic awareness to create hybrid systems capable of both extracting large quantities of data and processing that data in a way that is meaningful to users. The Web extra at http://youtu.be/pqI2qcigiCw is a video demonstrating Artificial Intelligence for Disaster Response (AIDR), a free, open source, and easy-to-use platform to filter and classify relevant microblog messages during humanitarian crises. [ABSTRACT FROM PUBLISHER]

The need for quick, timely, and accurate information is critical in emergency events. During mass emergencies, people assemble information from both official and unofficial sources. As digital access expands, people will increasingly incorporate information from digital sources into decision making and assess it against the local circumstances they experience. If we extrapolate what such behavior means for the future, we can see that information management under emergency conditions will need to become increasingly socially distributed. The key question then is how to assess the quality of information: how 'good' or 'bad' it is; whether it is 'misinformation' or 'disinformation.' Borrowing from Simon's notion of satisficing, the authors argue that people's assessment of information helpfulness and credibility is a function of the 'everyday analytic' skills they employ during mass emergencies. To facilitate the critical work of 'everyday analysts,' we outline a research agenda for the development of analytical support tools. [ABSTRACT FROM AUTHOR]

Crises and disasters have micro and macro social arrangements that differ from routine situations, as the field of disaster studies has described over its 100-year history. With increasingly pervasive information and communications technology and a changing political arena where terrorism is perceived as a major threat, the attention to crisis is high. Some of these new features of social life have created changes in disaster response that we are only beginning to understand. The University of Colorado is establishing an area of sociologically informed research and information and communications technology development in crisis informatics. This article reports on research that examines features of computer-mediated communication and information sharing activity during and after the April 16, 2007, crisis at Virginia Tech by members of the public. The authors consider consequences that these technology-supported social interactions have on emergency response and implications for methods in e-Social Science. [ABSTRACT FROM AUTHOR]

The article discusses the impact of social media usage such as Twitter during the March and April 2009 apparent flood danger to the Red River Valley, which separates the North Dakota and Minnesota in a region that extends across the U.S.-Canadian border into the province of Manitoba. It also assesses the credibility of public journalism and user-generated media. Further, it mentions that an analysis of tens of thousands of tweets sent during the flood threat period indicates mechanisms for information production, distribution and organization in use in the otherwise unmanageable information space of microblogging.

Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions. [ABSTRACT FROM AUTHOR]

Technology-mediated social participation systems can dramatically change the way science, government, healthcare, entertainment, and enterprise operate. Using research from several fields, we can learn how to design better TMSP systems and to better integrate lessons learned in practice back into theory. [ABSTRACT FROM PUBLISHER]