CrisisMMD: Multimodal Twitter Datasets from Natural Disasters

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.Comment: 9 page

Measuring Spatio-Temporal Responses to Hurricane Matthew Employing TwitGis

This study explores spatio-temporal responses to Hurricane Matthew across the US states by analyzing Twitter data. This study finds that people in different states and periods respond differently to Hurricane Matthew. For instance, people in the Midwest and Northeast regions show a high proportion of tweets in the pre-hurricane period. Those in the Southeast region demonstrate a high proportion of those in the hurricane period, and those in the West region show a high proportion of those in the post-hurricane period. This study also finds that people increase long distance trips (over 100 km) and decrease short distance trips (within 5 km, between 5 and 10 km, and between 10 and 25 km) in the hurricane period. Lastly, people show the most different displacements between the Twitter data and the theoretical model in the hurricane period

Social Media, Disasters, and Cultural Heritage: An Analysis of Twitter Images of the 2015 Nepal Earthquake

This article provides an understanding of the underlying themes and patterns in the photographic images of cultural heritage sites posted on Twitter immediately after the 2015 Nepal Earthquake. An analysis of 6,529 images available in the SMERP data set was carried out to identify and understand the main themes emerging from the discussion on Twitter regarding the damages to cultural heritage sites. Fewer than 10% of the tweets with images available in the data set have cultural heritage sites as the subject. Among them, six main themes emerged from the analysis presented. The dominant theme, with 67% of the heritage images posted, involves some kind of situational awareness where Twitter users aimed to communicate the state of heritage sites after the earthquake

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

112.social: Design and Evaluation of a Mobile Crisis App for Bidirectional Communication between Emergency Services and Citizens

Emergencies threaten human lives and overall societal continuity, whether or not the crises and disasters are induced by nature, such as earthquakes, floods and hurricanes, or by human beings, such as accidents, terror attacks and uprisings. In such situations, not only do citizens demand information about the damage and safe behaviour, but emergency services also require high quality information to improve situational awareness. For this purpose, there are currently two kinds of apps available: General-purpose apps, such as Facebook Safety Check or Twitter Alerts, already integrate safety features. Specific crisis apps, such as KATWARN in Germany or FEMA in the US, provide information on how to behave before, during and after emergencies, and capabilities for reporting incidents or receiving disaster warnings. In this paper, we analyse authorities’ and citizens’ information demands and features of crisis apps. Moreover, we present the concept, implementation and evaluation of a crisis app for incident reporting and bidirectional communication between authorities and citizens. Using the app, citizens may (1) report incidents by providing a category, description, location and multimedia files and (2) receive broadcasts and responses from authorities. Finally, we outline features, requirements and contextual factors for incident reporting and bidirectional communication via mobile app