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
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The CrisisMMD multimodal Twitter dataset consists of several thousands of manually annotated tweets and images collected during seven major natural disasters including earthquakes, hurricanes, wildfires, and floods that happened in the year 2017 across different parts of the World. The provided datasets include three types of annotations. Informative vs not-informative: Informative Not informative Don’t know or can’t judge Humanitarian categories Affected individuals Infrastructure and utility damage Injured or dead people Missing or found people Rescue, volunteering or donation effort Vehicle damage Other relevant information Not relevant or can’t judge Damage severity assessment Severe damage Mild damage Little or no damage Don’t know or can’t judge Please use alternate download link CrisisNLP, in case if you are having problem in downloading from this site. You can also get tweet ids and a tweet downloader tool too. The first objective towards the effective use of microblogging services such as Twitter for situational awareness during the emerging disasters is discovery of the disaster-related postings. Given the wide range of possible disasters, using a pre-selected set of disaster-related keywords for the discovery is suboptimal. An alternative that we focus on in this work is to train a classifier using a small set of labeled postings that are becoming available as a disaster is emerging. Our hypothesis is that utilizing large quantities of historical microblogs could improve the quality of classification, as compared to training a classifier only on the labeled data. We propose to use unlabeled microblogs to cluster words into a limited number of clusters and use the word clusters as features for classification. To evaluate the proposed semi-supervised approach, we used Twitter data from 6 different disasters. Our results indicate that when the number of labeled tweets is 100 or less, the proposed approach is superior to the standard classification based on the bag or words feature representation. Our results also reveal that the choice of the unlabeled corpus, the choice of word clustering algorithm, and the choice of hyperparameters can have a significant impact on the classification accuracy. 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) During mass convergence events such as natural disasters, microblogging platforms like Twitter are widely used by affected people to post situational awareness messages. These crisis-related messages disperse among multiple categories like infrastructure damage, information about missing, injured, and dead people etc. The challenge here is to extract important situational updates from these messages, assign them appropriate informational categories, and finally summarize big trove of information in each category. In this paper, we propose a novel framework which first assigns tweets into different situational classes and then summarize those tweets. In the summarization phase, we propose a two stage summarization framework which first extracts a set of important tweets from the whole set of information through an Integer-linear programming (ILP) based optimization technique and then follows a word graph and content word based abstractive summarization technique to produce the final summary. Our method is time and memory efficient and outperforms the baseline in terms of quality, coverage of events, , effectiveness, and utility in disaster scenarios.