TY - GEN
T1 - Twitter as a lifeline
T2 - 10th International Conference on Language Resources and Evaluation, LREC 2016
AU - Imran, Muhammad
AU - Mitra, Prasenjit
AU - Castillo, Carlos
PY - 2016
Y1 - 2016
N2 - Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.
AB - Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.
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M3 - Conference contribution
AN - SCOPUS:85031928421
T3 - Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
SP - 1638
EP - 1643
BT - Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
A2 - Calzolari, Nicoletta
A2 - Choukri, Khalid
A2 - Mazo, Helene
A2 - Moreno, Asuncion
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Grobelnik, Marko
A2 - Odijk, Jan
A2 - Piperidis, Stelios
A2 - Maegaard, Bente
A2 - Mariani, Joseph
PB - European Language Resources Association (ELRA)
Y2 - 23 May 2016 through 28 May 2016
ER -