During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this on- line information, if processed timely and effectively, is ex- tremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.
The use of social media such as Twitter, Facebook, and Youtube, has been explored in numerous studies @cite_17 @cite_14 @cite_20 @cite_10 @cite_16 for curating, analyzing and summarizing crisis-related information in order to make some decisions and responses. Current literature does not only highlight its importance but also provides directions for possible research avenues. Among them, one of the important research avenues is the exploitation of textual and visual content to extract useful information for humanitarian aid, which has been remained unexplored to a large extent. One of the important limitations of this line of research is the lack of ground-truth data. Below, we describe works that provide crisis-related datasets.
@cite_14 @cite_16 @cite_10 @cite_20 @cite_17
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We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques. Situation awareness plays an important role in disaster response and emergency management. Displaying real-time location-based social media messages along with videos, pictures, and hashtags during a disaster event could help first responders improve their situation awareness . A geo-targeted event observation (Geo) Viewer was developed for monitoring real-time social media messages in target areas with four major functions: (1) real-time display of geo-tagged tweets within the target area; (2) interactive mapping functions; (3) spatial, text, and temporal search functions using keywords, spatial boundaries, or dates; and (4) manual labeling and text-tagging of messages. Different from traditional web GIS maps, the user interface design of GeoViewer provides the interactive display of multimedia content and maps. The front-end user interface to visualize and query tweets is built with open source programming libraries using server-side MongoDB. GeoViewer is built for assisting emergency responses and disaster management tasks by tracking disaster event impacts, recovery activities, and residents’ needs in the target region. Today's crises attract great attention on social media, from local and distant citizens as well as from news media. This study investigates the possibilities of real-time and automated analysis of Twitter messages during crises. The analysis was performed through application of an information extraction tool to nearly 97,000 tweets that were published shortly before, during and after a storm hit the Pukkelpop 2011 festival in Belgium. As soon as the storm hit the festival tweet activity increased exponentially, peaking at 576 tweets per minute. The extraction tool enabled analyzing tweets through predefined (geo)graphical displays, message content filters (damage, casualties) and tweet type filters (e.g., retweets). Important topics that emerged were 'early warning tweets', 'rumors' and the 'self-organization of disaster relief' on Twitter. Results indicate that automated filtering of information provides valuable information for operational response and crisis communication. Steps for further research are discussed. © 2012 ISCRAM. Environmental Systems Research Institute, Inc. (ESRI) 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.