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.
In crisis informatics, one of the earliest and publicly-available datasets is CrisisLex @cite_21 . It consists of tweets collected during six disaster events occurred in USA, Australia, and Canada between October 2012 and July 2013. The dataset was collected using keywords and geo-graphical information from Twitter. The annotations of the dataset consist of i) directly related, ii) indirectly related, iii) not related, and iv) not in English or not understandable. In another work @cite_12 , the authors provide a dataset that consists of tweets from 26 crisis events that took place between 2012 and 2013. In this work, they first characterize the datasets along different crisis dimensions: 1) hazard type (i.e., natural vs. human-induced) and their subtypes, 2) temporal development (i.e., instantaneous vs. progressive), 3) geographic (i.e., focalized vs. diffused). Then, they characterized the datasets by 1) informativeness, 2) information type, and 3) source. Similar to the previous study they employed crowd-source workers to annotate the dataset. The dataset is publicly available at CrisisLex site http: crisislex.org .
@cite_21 @cite_12
641710284 2151098288
Keywords: Crisis Informatics ; Social Media Collection ; Social Media Analysis Reference EPFL-CONF-203561 Record created on 2014-11-26, modified on 2017-05-12 The use of social media to communicate timely information during crisis situations has become a common practice in recent years. In particular, the one-to-many nature of Twitter has created an opportunity for stakeholders to disseminate crisis-relevant messages, and to access vast amounts of information they may not otherwise have. Our goal is to understand what affected populations, response agencies and other stakeholders can expect-and not expect-from these data in various types of disaster situations. Anecdotal evidence suggests that different types of crises elicit different reactions from Twitter users, but we have yet to see whether this is in fact the case. In this paper, we investigate several crises-including natural hazards and human-induced disasters-in a systematic manner and with a consistent methodology. This leads to insights about the prevalence of different information types and sources across a variety of crisis situations.