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Microblogging platforms such as Twitter provide active communication channelsduring mass convergence and emergency events such as earthquakes, typhoons.During the sudden onset of a crisis situation, affected people post usefulinformation on Twitter that can be used for situational awareness and otherhumanitarian disaster response efforts, if processed timely and effectively.Processing social media information pose multiple challenges such as parsingnoisy, brief and informal messages, learning information categories from theincoming stream of messages and classifying them into different classes amongothers. One of the basic necessities of many of these tasks is the availabilityof data, in particular human-annotated data. In this paper, we presenthuman-annotated Twitter corpora collected during 19 different crises that tookplace between 2013 and 2015. To demonstrate the utility of the annotations, wetrain machine learning classifiers. Moreover, we publish first largest word2vecword embeddings trained on 52 million crisis-related tweets. To deal withtweets language issues, we present human-annotated normalized lexical resourcesfor different lexical variations.

Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages