Abstract
Information posted by people on Twitter during crises can significantly improve crisis response towards reducing human and financial loss. Deep learning algorithms can identify related tweets to reduce information overloaded which prevents humanitarian organizations from using Twitter posts. However, they heavily rely on labeled data which is unavailable for emerging crises. And because each crisis has its own features such as location, occurring time and social media response, current models are known to suffer from generalizing to unseen disaster events when pretrained on past ones. To solve this problem, we propose a domain adaptation approach that makes use of a distant supervision-based framework to label the unlabeled data from emerging crises. Then, pseudo-labeled target data, along with labeled-data from similar past disasters, are used to build the target model. Our results show that our approach can be seen as a general robust method to classify unseen tweets from current events.
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References
Qu, Y., Huang, C., Zhang, P., Zhang, J.: Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp. 25–34. ACM, March 2011
Starbird, K., Palen, L., Hughes, A.L., Vieweg, S.: Chatter on the red: what hazards threat reveals about the social life of microblogged information. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, pp. 241–250. ACM, February 2010
Vieweg, S.E.: Situational awareness in mass emergency: a behavioural and linguistic analysis of microblogged communications. Doctoral dissertation, University of Colorado at Boulder (2012)
Gao, H., Barbier, G., Goolsby, R.: Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell. Syst. 26(3), 10–14 (2011)
Caragea, C., Silvescu, A., Tapia, A.H.: Identifying informative messages in disaster events using convolutional neural networks. In: International Conference on Information Systems for Crisis Response and Management, pp. 137–147, May 2016
Nguyen, D.T., Mannai, K.A.A., Joty, S., Sajjad, H., Imran, M., Mitra, P.: Rapid classification of crisis-related data on social networks using convolutional neural networks. arXiv preprint arXiv:1608.03902 (2016)
Nguyen, D.T., Joty, S., Imran, M., Sajjad, H., Mitra, P.: Applications of online deep learning for crisis response using social media information. arXiv preprint arXiv:1610.01030 (2016)
Verma, S., Vieweg, S., Corvey, W.J., Palen, L., Martin, J.H., Palmer, M., Anderson, K.M.: Natural language processing to the rescue? Extracting “situational awareness” tweets during mass emergency. In: ICWSM, pp. 385–392, July 2011
Tapia, A.H., Moore, K.: Good enough is good enough: overcoming disaster response organizations’ slow social media data adoption. Comput. Support Coop. Work. (CSCW) 23(4–6), 483–512 (2014)
Ruder, S.: Neural Transfer Learning for Natural Language Processing. National University of Ireland, Galway (2019)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, pp. 1003–1011. Association for Computational Linguistics, August 2009
Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley framenet project. In: Proceedings of the 17th International Conference on Computational Linguistics, vol. 1, pp. 86–90. Association for Computational Linguistics, August 1998
Chu, C., Wang, R.: A survey of domain adaptation for neural machine translation. arXiv preprint arXiv:1806.00258 (2018)
Li, H., Guevara, N., Herndon, N., Caragea, D., Neppalli, K., Caragea, C., Neppalli, K., Caragea, C., Squicciarini, A.C., Tapia, A.H.: Twitter mining for disaster response: a domain adaptation approach. In: ISCRAM, May 2015
Li, H., Caragea, D., Caragea, C., Herndon, N.: Disaster response aided by tweet classification with a domain adaptation approach. J. Contingencies Crisis Manag. 26(1), 16–27 (2018)
Mazloom, R.: Classification of Twitter disaster data using a hybrid feature-instance adaptation approach. Doctoral dissertation (2018)
Alam, F., Joty, S., Imran, M.: Domain adaptation with adversarial training and graph embeddings. arXiv preprint arXiv:1805.05151 (2018)
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861 (2016)
Chen, Y., Liu, S., Zhang, X., Liu, K., Zhao, J.: Automatically labeled data generation for large scale event extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 409–419 (2017)
Zeng, Y., Feng, Y., Ma, R., Wang, Z., Yan, R., Shi, C., Zhao, D.: Scale up event extraction learning via automatic training data generation. arXiv preprint arXiv:1712.03665 (2017)
Mohammed, S., Ghelani, N., Lin, J.: Distant supervision for topic classification of tweets in curated streams. arXiv preprint arXiv:1704.06726 (2017)
Magdy, W., Sajjad, H., El-Ganainy, T., Sebastiani, F.: Distant supervision for tweet classification using YouTube labels. In: ICWSM, pp. 638–641, April 2015
Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)
Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)
Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. J. Doc. 28(1), 11–21 (1972)
Alrashdi, R., O’Keefe, S.: Deep learning and word embedding for tweet classification for crisis response. In: The 3rd National Computing Colleges Conference (NC3). arXiv preprint arXiv:1903.11024, October 2018
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Imran, M., Mitra, P., Castillo, C.: Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages. arXiv preprint arXiv:1605.05894 (2016)
Olteanu, A., Vieweg, S., Castillo, C.: What to expect when the unexpected happens: social media communications across crises. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 994–1009. ACM, February 2015
Olteanu, A., Castillo, C., Diaz, F., Vieweg, S.: CrisisLex: a lexicon for collecting and filtering microblogged communications in crises. In: Proceedings of the AAAI Conference on Weblogs and Social Media (ICWSM 2014). AAAI Press, Ann Arbor (2014)
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ALRashdi, R., O’Keefe, S. (2020). Robust Domain Adaptation Approach for Tweet Classification for Crisis Response. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_14
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