{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:29:57Z","timestamp":1781108997895,"version":"3.54.1"},"reference-count":44,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10,1]]},"abstract":"<p>Domain adaptation methods have been introduced for auto-filtering disaster tweets to address the issue of lacking labeled data for an emerging disaster. In this article, the authors present and compare two simple, yet effective approaches for the task of classifying disaster-related tweets. The first approach leverages the unlabeled target disaster data to align the source disaster distribution to the target distribution, and, subsequently, learns a supervised classifier from the modified source data. The second approach uses the strategy of self-training to iteratively label the available unlabeled target data, and then builds a classifier as a weighted combination of source and target-specific classifiers. Experimental results using Na\u00efve Bayes as the base classifier show that both approaches generally improve performance as compared to baseline. Overall, the self-training approach gives better results than the alignment-based approach. Furthermore, combining correlation alignment with self-training leads to better result, but the results of self-training are still better.<\/p>","DOI":"10.4018\/ijiscram.2018100101","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T12:57:30Z","timestamp":1565701050000},"page":"1-20","source":"Crossref","is-referenced-by-count":2,"title":["Domain Adaptation for Crisis Data Using Correlation Alignment and Self-Training"],"prefix":"10.4018","volume":"10","author":[{"given":"Hongmin","family":"Li","sequence":"first","affiliation":[{"name":"Kansas State University, Manhattan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleksandra","family":"Sopova","sequence":"additional","affiliation":[{"name":"Kansas State University, Manhattan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doina","family":"Caragea","sequence":"additional","affiliation":[{"name":"Kansas State University, Manhattan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cornelia","family":"Caragea","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJISCRAM.2018100101-0","unstructured":"Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., & Marchand, M. (2014). Domain-adversarial neural networks. Retrieved from http:\/\/arxiv.org\/abs\/1412.4446"},{"key":"IJISCRAM.2018100101-1","article-title":"Analysis of representations for domain adaptation","author":"S.Ben-David","year":"2007"},{"key":"IJISCRAM.2018100101-2","article-title":"Learning bounds for domain adaptation","author":"J.Blitzer","year":"2008"},{"key":"IJISCRAM.2018100101-3","unstructured":"Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. Association for computational linguistics."},{"key":"IJISCRAM.2018100101-4","unstructured":"Caragea, C., Silvescu, A., & Tapia, A. H. (2016). Identifying informative messages in disasters using convolutional neural networks. In Proceedings of the 13th proceedings of the international conference on information systems for crisis response and management, Rio de Janeiro, Brasil, May 22-25. Retrieved from http:\/\/idl.iscram.org\/files\/corneliacaragea\/2016\/1397C orneliaCarageaetal2016.pdf"},{"key":"IJISCRAM.2018100101-5","unstructured":"Caragea, C., Squicciarini, A. C., Stehle, S., Neppalli, K., & Tapia, A. H. (2014). Mapping moods: Geo-mapped sentiment analysis during hurricane sandy. In Proceedings of the 11th proceedings of the international conference on information systems for crisis response and management, University Park, PA, May 18-21."},{"key":"IJISCRAM.2018100101-6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781316476840"},{"key":"IJISCRAM.2018100101-7","unstructured":"Chen, M., Xu, Z. E., Weinberger, K. Q., & Sha, F. (2012). Marginalized denoising autoencoders for domain adaptation. Retrieved from http:\/\/arxiv.org\/abs\/1206.4683"},{"key":"IJISCRAM.2018100101-8","first-page":"540","article-title":"Transferring na\u00efve bayes classifiers for text classification.","volume":"Vol. 1,","author":"W.Dai","year":"2007","journal-title":"Proceedings of the 22nd national conference on artificial intelligence"},{"key":"IJISCRAM.2018100101-9","first-page":"256","article-title":"Frustratingly easy domain adaptation.","author":"H.Daum\u00e9","year":"2007","journal-title":"Proceedings of the 45th annual meeting of the association of computational linguistics"},{"key":"IJISCRAM.2018100101-10","unstructured":"Meier, P. (2013, May 2). Crisis maps: Harnessing the power of big data to deliver humanitarian assistance. Forbes."},{"key":"IJISCRAM.2018100101-11","unstructured":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., . . . Lempitsky, V. S. (2015). Domain-adversarial training of neural networks. Retrieved from http:\/\/arxiv.org\/abs\/1505.07818"},{"key":"IJISCRAM.2018100101-12","doi-asserted-by":"publisher","DOI":"10.1145\/2487788.2488109"},{"key":"IJISCRAM.2018100101-13","unstructured":"Imran, M., Mitra, P., & Srivastava, J. (2016). Cross-language domain adaptation for classifying crisis-related short messages. In Proceedings of the ISCRAM 2016 conference, Rio de Janeiro, Brazil. Academic Press."},{"key":"IJISCRAM.2018100101-14","first-page":"264","article-title":"Instance weighting for domain adaptation in NLP.","author":"J.Jiang","year":"2007","journal-title":"Proceedings of the 45th annual meeting of the association of computational linguistics"},{"key":"IJISCRAM.2018100101-15","doi-asserted-by":"publisher","DOI":"10.1145\/1321440.1321498"},{"key":"IJISCRAM.2018100101-16","unstructured":"Li, H., Caragea, D., & Caragea, C. (2017). Towards practical usage of a domain adaptation algorithm in the early hours of a disaster. In Proceedings of the 14th ISCRAM conference, Albi, France, May. Academic Press."},{"key":"IJISCRAM.2018100101-17","doi-asserted-by":"publisher","DOI":"10.1111\/1468-5973.12194"},{"key":"IJISCRAM.2018100101-18","unstructured":"Li, H., Guevara, N., Herndon, N., Caragea, D., Neppalli, K., Caragea, C., . . . Tapia, A. H. (2015). Twitter Mining for Disaster Response: A Domain Adaptation Approach. In Proceedings of the 12th international conference on information systems for crisis response and management. Academic Press."},{"key":"IJISCRAM.2018100101-19","unstructured":"Li, H., Li, X., Caragea, D., & Caragea, C. (2018). Comparison of word embeddings and sentence encodings as generalized representations for crisis tweet classification tasks. Retrieved from https:\/\/www.cs.uic.edu\/cornelia\/papers\/iscramasian18.pdf"},{"key":"IJISCRAM.2018100101-20","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071"},{"key":"IJISCRAM.2018100101-21","unstructured":"Maron, D. F. (2013). How social media is changing disaster response. Scientific American. Retrieved from https:\/\/www.scientificamerican.com\/article\/how-social-media-is-changing-disaste"},{"key":"IJISCRAM.2018100101-22","unstructured":"Mazloom, R., Li, H., Caragea, D., Imran, M., & Caragea, C. (2018). Classification of twitter disaster data using a hybrid feature-instance adaptation approach. Retrieved from http:\/\/mimran.me\/papers\/MazloometalISCRAM 2018.pdf"},{"key":"IJISCRAM.2018100101-23","doi-asserted-by":"crossref","unstructured":"Mendoza, M., Poblete, B., & Castillo, C. (2010). Twitter under crisis: Can we trust what we rt? In Proceedings of the first workshop on social media analytics (soma \u201910) (pp. 71\u201379). Academic Press.","DOI":"10.1145\/1964858.1964869"},{"key":"IJISCRAM.2018100101-24","author":"T.Mitchell","year":"1997","journal-title":"Machine learning"},{"key":"IJISCRAM.2018100101-25","unstructured":"Mitchell, T. (2017). Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Retrieved from http:\/\/www.cs.cmu.edu\/ tom\/mlbook\/NBayesLogReg.pdf"},{"key":"IJISCRAM.2018100101-26","unstructured":"National Research Council. (2013). Public response to alerts and warnings using social media: Report of a workshop on current knowledge and research gaps. Washington, DC: The National Academies Press. Retrieved from https:\/\/www.nap.edu\/catalog\/15853\/public-response-to-alerts-and-warnings-using-"},{"key":"IJISCRAM.2018100101-27","unstructured":"Nguyen, D. T., Al-Mannai, K., Joty, S. R., Sajjad, H., Imran, M., & Mitra, P. (2016). Rapid classification of crisis-related data on social networks using convolutional neural networks. Retrieved from http:\/\/arxiv.org\/abs\/1608.03902"},{"key":"IJISCRAM.2018100101-28","doi-asserted-by":"crossref","unstructured":"Olteanu, A., Castillo, C., Diaz, F., & Vieweg, S. (2014). Crisislex: A lexicon for collecting and filtering microblogged communications in crises. In Proceedings of the eighth international conference on weblogs and social media, ICWSM 2014, Ann Arbor, Michigan, June 1-4. Academic Press. Retrieved from http:\/\/www.aaai.org\/ocs\/index.php\/ICWSM\/ICWSM14\/paper\/view\/8091","DOI":"10.1609\/icwsm.v8i1.14538"},{"key":"IJISCRAM.2018100101-29","unstructured":"Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2009). Domain adaptation via transfer component analysis. In Proceedings of the 21st international joint conference on artificial intelligence (pp. 1187\u20131192). Academic Press."},{"key":"IJISCRAM.2018100101-30","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"IJISCRAM.2018100101-31","doi-asserted-by":"crossref","unstructured":"Qadir, J., Ali, A., Rasool, R. U., Zwitter, A., Sathiaseelan, A., & Crowcroft, J. (2016). Crisis analytics: Big data driven crisis response.","DOI":"10.1186\/s41018-016-0013-9"},{"key":"IJISCRAM.2018100101-32","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2018.1427832"},{"key":"IJISCRAM.2018100101-33","doi-asserted-by":"publisher","DOI":"10.1111\/1468-5973.12196"},{"key":"IJISCRAM.2018100101-34","unstructured":"Scikit-Learn. (2016). Variance threshold. Retrieved from http:\/\/scikit-learn.org"},{"key":"IJISCRAM.2018100101-35","author":"O.Sopova","year":"2017","journal-title":"Domain adaptation for classifying disaster-related twitter data"},{"key":"IJISCRAM.2018100101-36","doi-asserted-by":"crossref","unstructured":"Starbird, K., Palen, L., Hughes, A., & Vieweg, S. (2010). Chatter on the red: What hazards threat reveals about the social life of microblogged information. In Proceedings of the ACM 2008 conference on computer supported cooperative work (CSCW2010) (pp. 241\u2013250). Academic Press.","DOI":"10.1145\/1718918.1718965"},{"key":"IJISCRAM.2018100101-37","doi-asserted-by":"publisher","DOI":"10.1111\/1468-5973.12193"},{"key":"IJISCRAM.2018100101-38","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., & Saenko, K. (2016). Return of frustratingly easy domain adaptation. In Proceedings of the thirtieth AAAI conference on artificial intelligence (AAAI-16) (pp. 2058\u20132065). AAAI.","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"IJISCRAM.2018100101-39","doi-asserted-by":"crossref","unstructured":"Sun, B., & Saenko, K. (2016). Deep CORAL: correlation alignment for deep domain adaptation. Retrieved from http:\/\/arxiv.org\/abs\/1607.01719","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"IJISCRAM.2018100101-40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00958-7_31"},{"key":"IJISCRAM.2018100101-41","doi-asserted-by":"publisher","DOI":"10.1111\/1468-5973.12141"},{"key":"IJISCRAM.2018100101-42","doi-asserted-by":"publisher","DOI":"10.3115\/981658.981684"},{"key":"IJISCRAM.2018100101-43","unstructured":"Zhang, Y., Drake, W., Li, Y., Zobel, C. W., & Cowell, M. (2015). Fostering community resilience through adaptive learning in a social media age: Municipal twitter use in new jersey following hurricane sandy. In L. Palen, M. B\u00fcscher, T. Comes, & A. L. Hughes (Eds.), 12th proceedings of the international conference on information systems for crisis response and management, Krystiansand, Norway, May 24-27. ISCRAM Association."}],"container-title":["International Journal of Information Systems for Crisis Response and Management"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=235417","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T22:24:44Z","timestamp":1695075884000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJISCRAM.2018100101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2018,10,1]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijiscram.2018100101","relation":{},"ISSN":["1937-9390","1937-9420"],"issn-type":[{"value":"1937-9390","type":"print"},{"value":"1937-9420","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,1]]}}}