Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma

References

• Acar, A., and Y. Muraki. 2011. “Twitter for Crisis Communication: Lessons Learned from Japan’s Tsunami Disaster.” International Journal of Web Based Communities 7 (3): 392–402. doi: 10.1504/IJWBC.2011.041206
   Google Scholar
• Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. “Latent Dirichlet Allocation.” The Journal of Machine Learning Research 3: 993–1022.
   Web of Science ®Google Scholar
• Burel, G., H. Saif, and H. Alani. 2017. “Semantic Wide and Deep Learning for Detecting Crisis-information Categories on Social Media.” Paper presented at the International Semantic Web Conference.
   Google Scholar
• Burel, G., H. Saif, M. Fernandez, and H. Alani. 2017. “On Semantics and Deep Learning for Event Detection in Crisis Situations.”  In Workshop on Semantic Deep Learning (SemDeep), at ESWC 2017, 29 May 2017, Portoroz, Slovenia. 
   Google Scholar
• Byeon, W., T. M. Breuel, F. Raue, and M. Liwicki. 2015. “Scene Labeling with Lstm Recurrent Neural Networks.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
   Google Scholar
• Caragea, C., N. McNeese, A. Jaiswal, G. Traylor, H.-W. Kim, P. Mitra, and B. J. Jansen. 2011. “Classifying Text Messages for the Haiti Earthquake.” Paper presented at the Proceedings of the 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM2011).
   Google Scholar
• Caragea, C., A. Silvescu, and A. H. Tapia. 2016. “Identifying Informative Messages in Disaster Events Using Convolutional Neural Networks.” Paper presented at the International Conference on Information Systems for Crisis Response and Management.
   Google Scholar
• Cervone, G., E. Sava, Q. Huang, E. Schnebele, J. Harrison, and N. Waters. 2016. “Using Twitter for Tasking Remote-sensing Data Collection and Damage Assessment: 2013 Boulder Flood Case Study.” International Journal of Remote Sensing 37 (1): 100–124. doi: 10.1080/01431161.2015.1117684
   Web of Science ®Google Scholar
• De Albuquerque, J. P., B. Herfort, A. Brenning, and A. Zipf. 2015. “A Geographic Approach for Combining Social Media and Authoritative Data Towards Identifying Useful Information for Disaster Management.” International Journal of Geographical Information Science 29 (4): 667–689. doi: 10.1080/13658816.2014.996567
   Web of Science ®Google Scholar
• De Choudhury, M., N. Diakopoulos, and M. Naaman. 2012. “Unfolding the Event Landscape on Twitter: Classification and Exploration of User Categories.” Paper presented at the Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work.
   Google Scholar
• Demir, I., H. Conover, W. F. Krajewski, B.-C. Seo, R. Goska, Y. He, and W. Petersen. 2015. “Data-enabled Field Experiment Planning, Management, and Research Using Cyberinfrastructure.” Journal of Hydrometeorology 16 (3): 1155–1170. doi: 10.1175/JHM-D-14-0163.1
   Web of Science ®Google Scholar
• Doggett, E. V., and A. Cantarero. 2016. “Identifying Eyewitness News-worthy Events on Twitter.” Paper presented at the Conference on Empirical Methods in Natural Language Processing.
   Google Scholar
• Ester, M., H.-P. Kriegel, J. Sander, and X. Xu. 1996. “A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.” Paper presented at the Knowledge Discovery and Data Mining (KDD), Portland, OR.
   Google Scholar
• Fang, R., A. Nourbakhsh, X. Liu, S. Shah, and Q. Li. 2016. “Witness Identification in Twitter.” Paper presented at the Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media, Austin, TX, USA.
   Google Scholar
• Feng, Y., and M. Sester. 2018. “Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos.” ISPRS International Journal of Geo-Information 7 (2): 39. doi: 10.3390/ijgi7020039
   Web of Science ®Google Scholar
• Goldstone, A., and T. Underwood. 2012. “What Can Topic Models of PMLA Teach us about the History of Literary Scholarship.” Journal of Digital Humanities 2 (1): 39–48.
   Google Scholar
• Hagen, L., T. Keller, S. Neely, N. DePaula, and C. Robert-Cooperman. 2017. “Crisis Communications in the Age of Social Media: A Network Analysis of Zika-related Tweets.” Social Science Computer Review 36 (5): 523–541. doi: 10.1177/0894439317721985
   Web of Science ®Google Scholar
• Herfort, B., J. P. de Albuquerque, S.-J. Schelhorn, and A. Zipf. 2014. “Exploring the Geographical Relations Between Social Media and Flood Phenomena to Improve Situational Awareness.” In Connecting a Digital Europe Through Location and Place, edited by Joaquin Huerta, Sven Schade, and Carlos Granell, 55–71. Heidelberg: Springer.
   Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-term Memory.” Neural Computation 9 (8): 1735–1780. doi: 10.1162/neco.1997.9.8.1735
   PubMed Web of Science ®Google Scholar
• Huang, Q., G. Cervone, and G. Zhang. 2017. “A Cloud-enabled Automatic Disaster Analysis System of Multi-sourced Data Streams: An Example Synthesizing Social Media, Remote Sensing and Wikipedia Data.” Computers, Environment and Urban Systems 66: 23–37. doi: 10.1016/j.compenvurbsys.2017.06.004
   Web of Science ®Google Scholar
• Huang, X., C. Wang, Z. Li, and H. Ning. 2018. “A Visual–Textual Fused Approach to Automated Tagging of Flood-related Tweets During a Flood Event.” International Journal of Digital Earth, 1–17. doi:10.1080/17538947.2018.1523956.
   Google Scholar
• Imran, M., S. Elbassuoni, C. Castillo, F. Diaz, and P. Meier. 2013. “Extracting Information Nuggets from Disaster-related Messages in Social Media.” Paper presented at the ISCRAM.
   Google Scholar
• Kanhabua, N., and W. Nejdl. 2013. “Understanding the Diversity of Tweets in the Time of Outbreaks.” Paper presented at the Proceedings of the 22nd International Conference on World Wide Web.
   Google Scholar
• Kim, Y. 2014. “Convolutional Neural Networks for Sentence Classification.” arXiv preprint arXiv:1408.5882.
   Google Scholar
• Kongthon, A., C. Haruechaiyasak, J. Pailai, and S. Kongyoung. 2012. “The Role of Twitter During a Natural Disaster: Case Study of 2011 Thai Flood.” Paper presented at the Technology Management for Emerging Technologies (PICMET), 2012 Proceedings of PICMET’12.
   Google Scholar
• Koylu, C. 2018a. “Modeling and Visualizing Semantic and Spatio-temporal Evolution of Topics in Interpersonal Communication on Twitter.” International Journal of Geographical Information Science: 1–28. doi:10.1080/13658816.2018.1458987.
   Web of Science ®Google Scholar
• Koylu, C. 2018b. “Uncovering Geo-social Semantics from the Twitter Mention Network: An Integrated Approach Using Spatial Network Smoothing and Topic Modeling.” In Human Dynamics Research in Smart and Connected Communities, edited by S.-L. Shaw, and D. Sui, 163–179. Cham: Springer International.
   Google Scholar
• Krajewski, W. F., D. Ceynar, I. Demir, R. Goska, A. Kruger, C. Langel, and B.-C. Seo. 2017. “Real-time Flood Forecasting and Information System for the State of Iowa.” Bulletin of the American Meteorological Society 98 (3): 539–554. doi: 10.1175/BAMS-D-15-00243.1
   Web of Science ®Google Scholar
• Kryvasheyeu, Y., H. Chen, N. Obradovich, E. Moro, P. Van Hentenryck, J. Fowler, and M. Cebrian. 2016. “Rapid Assessment of Disaster Damage Using Social Media Activity.” Science Advances 2 (3): e1500779. doi: 10.1126/sciadv.1500779
   PubMed Web of Science ®Google Scholar
• Kumar, S., F. Morstatter, R. Zafarani, and H. Liu. 2013. “Whom Should I Follow?: Identifying Relevant Users During Crises.” Paper presented at the Proceedings of the 24th ACM Conference on Hypertext and Social Media.
   Google Scholar
• Kwon, H. Y., and Y. O. Kang. 2016. “Risk Analysis and Visualization for Detecting Signs of Flood Disaster in Twitter.” Spatial Information Research 24 (2): 127–139. doi:10.1007/s41324-016-0014-1.
   Web of Science ®Google Scholar
• Lachlan, K. A., P. R. Spence, X. Lin, K. Najarian, and M. Del Greco. 2016. “Social Media and Crisis Management: CERC, Search Strategies, and Twitter Content.” Computers in Human Behavior 54: 647–652. doi: 10.1016/j.chb.2015.05.027
   Web of Science ®Google Scholar
• Li, Z., C. Wang, C. T. Emrich, and D. Guo. 2018. “A Novel Approach to Leveraging Social Media for Rapid Flood Mapping: A Case Study of the 2015 South Carolina Floods.” Cartography and Geographic Information Science 45 (2): 97–110. doi:10.1080/15230406.2016.1271356.
   Web of Science ®Google Scholar
• Liu, S. B., L. Palen, J. Sutton, A. Hughes, and S. Vieweg. 2008. “In Search of the Bigger Picture: The Emergent Role of On-line Photo Sharing in Times of Disaster.” Paper presented at the Proceedings of the Information Systems for Crisis Response and Management Conference (ISCRAM).
   Google Scholar
• Liu, P., X. Qiu, and X. Huang. 2016. “Recurrent Neural Network for Text Classification with Multi-task Learning.” arXiv preprint arXiv:1605.05101.
   Google Scholar
• Maantay, J., A. Maroko, and G. Culp. 2010. “Using Geographic Information Science to Estimate Vulnerable Urban Populations for Flood Hazard and Risk Assessment in New York City.” In Geospatial Techniques in Urban Hazard and Disaster Analysis. Vol. 2., edited by P. S. Showalter and Y. Lu, 71–97. Dordrecht: Springer.
   Google Scholar
• McCallum, A. K. 2002. MALLET: A Machine Learning for Language Toolkit [online]. Available from: http://mallet.cs.umass.edu [Accessed 28 Dec 2018]
   Google Scholar
• Monroy-Hernández, A., E. Kiciman, M. De Choudhury, and S. Counts. 2013. “The New War Correspondents: The Rise of Civic Media Curation in Urban Warfare.” Paper presented at the Proceedings of the 2013 Conference on Computer Supported Cooperative Work.
   Google Scholar
• Munro, R., and C. D. Manning. 2012. “Short Message Communications: Users, Topics, and In-language Processing.” Paper presented at the Proceedings of the 2nd ACM Symposium on Computing for Development.
   Google Scholar
• Nelson, L. D., P. R. Spence, and K. A. Lachlan. 2009. “Learning from the Media in the Aftermath of a Crisis: Findings from the Minneapolis Bridge Collapse.” Electronic News 3 (4): 176–192. doi: 10.1080/19312430903300046
   Google Scholar
• Newman, D., J. H. Lau, K. Grieser, and T. Baldwin. 2010. “Automatic Evaluation of Topic Coherence.” Paper presented at the Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics.
   Google Scholar
• Nguyen, D. T., S. Joty, M. Imran, H. Sajjad, and P. Mitra. 2016. “Applications of Online Deep Learning for Crisis Response Using Social Media Information.” arXiv preprint arXiv:1610.01030.
   Google Scholar
• Olteanu, A., C. Castillo, F. Diaz, and S. Vieweg. 2014. “CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises.” Paper presented at the ICWSM.
   Google Scholar
• Olteanu, A., S. Vieweg, and C. Castillo. 2015. “What to Expect When the Unexpected Happens: Social Media Communications Across Crises.” Paper presented at the Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing.
   Google Scholar
• Pekar, V., J. Binner, H. Najafi, and C. Hale. 2016. “Selecting Classification Features for Detection of Mass Emergency Events on Social Media.” Paper presented at the Proceedings of the International Conference on Security and Management (SAM).
   Google Scholar
• Restrepo-Estrada, C., S. C. de Andrade, N. Abe, M. C. Fava, E. M. Mendiondo, and J. P. de Albuquerque. 2018. “Geo-social Media as a Proxy for Hydrometeorological Data for Streamflow Estimation and to Improve Flood Monitoring.” Computers & Geosciences 111: 148–158. doi: 10.1016/j.cageo.2017.10.010
   Web of Science ®Google Scholar
• Schnebele, E. 2013. “Improving Remote Sensing Flood Assessment Using Volunteered Geographical Data.” Natural Hazards and Earth System Sciences 13 (3): 669. doi: 10.5194/nhess-13-669-2013
   Web of Science ®Google Scholar
• Schofield, A., and D. Mimno. 2016. “Comparing Apples to Apple: The Effects of Stemmers on Topic Models.” Transactions of the Association for Computational Linguistics 4: 287–300. doi: 10.1162/tacl_a_00099
   Google Scholar
• Smith, L., Q. Liang, P. James, and W. Lin. 2015. “Assessing the Utility of Social Media as a Data Source for Flood Risk Management Using a Real-time Modelling Framework.” Journal of Flood Risk Management 10 (3): 370–380. doi: 10.1111/jfr3.12154
   Web of Science ®Google Scholar
• Spielhofer, T., R. Greenlaw, D. Markham, and A. Hahne. 2016. “Data Mining Twitter During the UK Floods: Investigating the Potential Use of Social Media in Emergency Management.” Paper presented at the Information and Communication Technologies for Disaster Management (ICT-DM), 2016 3rd International Conference on.
   Google Scholar
• Stefanidis, A., A. Cotnoir, A. Croitoru, A. Crooks, M. Rice, and J. Radzikowski. 2013. “Demarcating New Boundaries: Mapping Virtual Polycentric Communities Through Social Media Content.” Cartography and Geographic Information Science 40 (2): 116–129. doi:10.1080/15230406.2013.776211.
   Web of Science ®Google Scholar
• Tiwari, C., and G. Rushton. 2005. “Using Spatially Adaptive Filters to Map Late Stage Colorectal Cancer Incidence in Iowa.” In Developments in Spatial Data Handling, edited by F. Peter Fisher, 665–676. Berlin: Springer.
   Google Scholar
• Vieweg, S., A. L. Hughes, K. Starbird, and L. Palen. 2010. “Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness.” Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
   Google Scholar
• Xingjian, S., Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo. 2015. “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.” Paper presented at the Advances in Neural Information Processing Systems.
   Google Scholar
• Zhang, Y., C. Szabo, and Q. Z. Sheng. 2016. “Improving Object and Event Monitoring on Twitter Through Lexical Analysis and User Profiling.” Paper presented at the International Conference on Web Information Systems Engineering.
   Google Scholar