Robust Domain Adaptation Approach for Tweet Classification for Crisis Response

  • Reem ALRashdiEmail author
  • Simon O’Keefe
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


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.


Domain adaptation Twitter data Crisis response Distant supervision 


  1. 1.
    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 2011Google Scholar
  2. 2.
    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 2010Google Scholar
  3. 3.
    Vieweg, S.E.: Situational awareness in mass emergency: a behavioural and linguistic analysis of microblogged communications. Doctoral dissertation, University of Colorado at Boulder (2012)Google Scholar
  4. 4.
    Gao, H., Barbier, G., Goolsby, R.: Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell. Syst. 26(3), 10–14 (2011)CrossRefGoogle Scholar
  5. 5.
    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 2016Google Scholar
  6. 6.
    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)
  7. 7.
    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)
  8. 8.
    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 2011Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    Ruder, S.: Neural Transfer Learning for Natural Language Processing. National University of Ireland, Galway (2019)Google Scholar
  11. 11.
    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 2009Google Scholar
  12. 12.
    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 1998Google Scholar
  13. 13.
    Chu, C., Wang, R.: A survey of domain adaptation for neural machine translation. arXiv preprint arXiv:1806.00258 (2018)
  14. 14.
    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 2015Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Mazloom, R.: Classification of Twitter disaster data using a hybrid feature-instance adaptation approach. Doctoral dissertation (2018)Google Scholar
  17. 17.
    Alam, F., Joty, S., Imran, M.: Domain adaptation with adversarial training and graph embeddings. arXiv preprint arXiv:1805.05151 (2018)
  18. 18.
    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)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861 (2016)
  20. 20.
    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)Google Scholar
  21. 21.
    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)
  22. 22.
    Mohammed, S., Ghelani, N., Lin, J.: Distant supervision for topic classification of tweets in curated streams. arXiv preprint arXiv:1704.06726 (2017)
  23. 23.
    Magdy, W., Sajjad, H., El-Ganainy, T., Sebastiani, F.: Distant supervision for tweet classification using YouTube labels. In: ICWSM, pp. 638–641, April 2015Google Scholar
  24. 24.
    Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)CrossRefGoogle Scholar
  25. 25.
    Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)Google Scholar
  26. 26.
    Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. J. Doc. 28(1), 11–21 (1972)CrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  29. 29.
    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)Google Scholar
  30. 30.
    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)
  31. 31.
    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 2015Google Scholar
  32. 32.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of YorkYorkUK
  2. 2.CSSE DepartmentUniversity of HailHailSaudi Arabia

Personalised recommendations