Twitter as a lifeline

Human-annotated Twitter corpora for NLP of crisis-related messages

Muhammad Imran, Prasenjit Mitra, Carlos Castillo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
PublisherEuropean Language Resources Association (ELRA)
Pages1638-1643
Number of pages6
ISBN (Electronic)9782951740891
Publication statusPublished - 1 Jan 2016
Event10th International Conference on Language Resources and Evaluation, LREC 2016 - Portoroz, Slovenia
Duration: 23 May 201628 May 2016

Other

Other10th International Conference on Language Resources and Evaluation, LREC 2016
CountrySlovenia
CityPortoroz
Period23/5/1628/5/16

Fingerprint

twitter
social media
learning
disaster
natural disaster
event
communication
language
resources
Natural Language Processing

Keywords

  • Disaster response
  • Natural language processing
  • Supervised classification
  • Twitter

ASJC Scopus subject areas

  • Linguistics and Language
  • Library and Information Sciences
  • Language and Linguistics
  • Education

Cite this

Imran, M., Mitra, P., & Castillo, C. (2016). Twitter as a lifeline: Human-annotated Twitter corpora for NLP of crisis-related messages. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016 (pp. 1638-1643). European Language Resources Association (ELRA).

Twitter as a lifeline : Human-annotated Twitter corpora for NLP of crisis-related messages. / Imran, Muhammad; Mitra, Prasenjit; Castillo, Carlos.

Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA), 2016. p. 1638-1643.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Imran, M, Mitra, P & Castillo, C 2016, Twitter as a lifeline: Human-annotated Twitter corpora for NLP of crisis-related messages. in Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA), pp. 1638-1643, 10th International Conference on Language Resources and Evaluation, LREC 2016, Portoroz, Slovenia, 23/5/16.
Imran M, Mitra P, Castillo C. Twitter as a lifeline: Human-annotated Twitter corpora for NLP of crisis-related messages. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA). 2016. p. 1638-1643
Imran, Muhammad ; Mitra, Prasenjit ; Castillo, Carlos. / Twitter as a lifeline : Human-annotated Twitter corpora for NLP of crisis-related messages. Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA), 2016. pp. 1638-1643
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