Search

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]

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]