Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features

References

• Acar, Adam, and Yuya Muraki. 2011. “Twitter for Crisis Communication: Lessons Learned from Japan's Tsunami Disaster.” International Journal of Web Based Communities 7 (3): 392–402.
   Google Scholar
• Alberts, Timothy A., Phillip B. Chilson, B. L. Cheong, and R. D. Palmer. 2011. “Evaluation of Weather Radar with Pulse Compression: Performance of a Fuzzy Logic Tornado Detection Algorithm.” Journal of Atmospheric and Oceanic Technology 28 (3): 390–400.
   Web of Science ®Google Scholar
• Ansari, Steve, S. Del Greco, and B. Hankins. 2010. “The Weather and Climate Toolkit.” Paper Presented at the AGU Fall Meeting Abstracts, San Francisco, CA, December 13–17.
   Google Scholar
• Ashktorab, Zahra, Christopher Brown, Manojit Nandi, and Aron Culotta. 2014. “Tweedr: Mining Twitter to Inform Disaster Response.” Paper Presented at the 11th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2014), University Park, PA.
   Google Scholar
• Bakillah, Mohamed, Ren-Yu Li, and Steve H. L. Liang. 2015. “Geo-Located Community Detection in Twitter with Enhanced Fast-Greedy Optimization of Modularity: The Case Study of Typhoon Haiyan.” International Journal of Geographical Information Science 29 (2): 258–279.
   Web of Science ®Google Scholar
• Banker, Kyle. 2011. MongoDB in Action. Shelter Island, NY: Manning Publications.
   Google Scholar
• Beaufort Wind Scale. 2021. “Beaufort Wind Scale.” https://www.weather.gov/tbw/beaufort.
   Google Scholar
• Blake, E. S., T. B. Kimberlain, R. J. Berg, J. P. Cangialosi, and J. L. Beven II. 2013. “Tropical Cyclone Report: Hurricane Sandy.” National Hurricane Center 12: 1–10.
   Google Scholar
• Bruns, Axel, and Yuxian Eugene Liang. 2012. “Tools and Methods for Capturing Twitter Data During Natural Disasters.” First Monday 17 (4). doi:10.5210/fm.v17i4.3937.
   Google Scholar
• Crooks, Andrew, Arie Croitoru, Anthony Stefanidis, and Jacek Radzikowski. 2013. “# Earthquake: Twitter as a Distributed Sensor System.” Transactions in GIS 17 (1): 124–147.
   Web of Science ®Google Scholar
• de Albuquerque, João Porto, Benjamin Herfort, Alexander Brenning, and Alexander 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.
   Web of Science ®Google Scholar
• De Longueville, Bertrand, Robin S. Smith, and Gianluca Luraschi. 2009. “Omg, from Here, I Can See the Flames! A Use Case of Mining Location Based Social Networks to Acquire Spatio-Temporal Data on Forest Fires.” Paper Presented at the Proceedings of the 2009 International Workshop on Location Based Social Networks, Seattle, WA, November 3.
   Google Scholar
• Ester, Martin, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.” Paper Presented at the Second International Conference on Knowledge Discovery and Data Mining (KDD), Portland, OR, August 2–4.
   Google Scholar
• Ford, R. 2011. “Earthquake: Twitter Users Learned of Tremors Seconds Before Feeling Them.” The Hollywood Reporter, August 23.
   Google Scholar
• Fuchs, Georg, Natalia Andrienko, Gennady Andrienko, Sebastian Bothe, and Hendrik Stange. 2013. “Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned.” Paper Presented at the Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, Orlando, FL, November 5.
   Google Scholar
• Gao, Huiji, Geoffrey Barbier, and Rebecca Goolsby. 2011. “Harnessing the Crowdsourcing Power of Social Media for Disaster Relief.” IEEE Intelligent Systems 3: 10–14.
   Google Scholar
• GDAL. 2021. “GDAL Documentation.” https://gdal.org/.
   Google Scholar
• Goodchild, Michael F., and J. Alan Glennon. 2010. “Crowdsourcing Geographic Information for Disaster Response: A Research Frontier.” International Journal of Digital Earth 3 (3): 231–241.
   Web of Science ®Google Scholar
• Goodchild, Michael F., and Linna Li. 2012. “Assuring the Quality of Volunteered Geographic Information.” Spatial Statistics 1: 110–120.
   Web of Science ®Google Scholar
• Halteren, Hans van, Jakub Zavrel, and Walter Daelemans. 2001. “Improving Accuracy in Word Class Tagging Through the Combination of Machine Learning Systems.” Computational Linguistics 27 (2): 199–229.
   Web of Science ®Google Scholar
• Houston, J. Brian, Joshua Hawthorne, Mildred F. Perreault, Eun Hae Park, Marlo Goldstein Hode, Michael R. Halliwell, Sarah E. Turner McGowen, Rachel Davis, Shivani Vaid, and Jonathan A. McElderry. 2015. “Social Media and Disasters: A Functional Framework for Social Media Use in Disaster Planning, Response, and Research.” Disasters 39 (1): 1–22.
   PubMed Web of Science ®Google Scholar
• Huang, Qunying, Guido Cervone, Duangyang Jing, and Chaoyi Chang. 2015. “DisasterMapper: A CyberGIS Framework for Disaster Management Using Social Media Data.” Paper Presented at the Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data, Bellevue, WA, November 3–6.
   Google Scholar
• Huang, Xiao, Cuizhen Wang, and Zhenlong Li. 2018a. “A Near Real-Time Flood-Mapping Approach by Integrating Social Media and Post-Event Satellite Imagery.” Annals of GIS 24 (2): 113–123.
   Web of Science ®Google Scholar
• Huang, Xiao, Cuizhen Wang, and Zhenlong Li. 2018b. “Reconstructing Flood Inundation Probability by Enhancing Near Real-Time Imagery with Real-Time Gauges and Tweets.” IEEE Transactions on Geoscience and Remote Sensing 56 (8): 4691–4701.
   Web of Science ®Google Scholar
• Huang, Xiao, Cuizhen Wang, Zhenlong Li, and Huan Ning. 2019. “A Visual–Textual Fused Approach to Automated Tagging of Flood-Related Tweets During a Flood Event.” International Journal of Digital Earth 12 (11): 1248–1264.
   Web of Science ®Google Scholar
• Huang, Qunying, and Yu Xiao. 2015. “Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery.” International Journal of Geo-Information 4 (3): 19. doi:https://doi.org/10.3390/ijgi4031549.
   Google Scholar
• Huang, Qunying, and Chen Xu. 2014. “A Data-Driven Framework for Archiving and Exploring Social Media Data.” Annals of GIS 20 (4): 265–277.
   Google Scholar
• Imran, Muhammad, Shady Elbassuoni, Carlos Castillo, Fernando Diaz, and Patrick Meier. 2013. “Practical Extraction of Disaster-Relevant Information from Social Media.” Paper Presented at the Proceedings of the 22nd International Conference on World Wide Web Companion, Rio de Janeiro, May 13–17.
   Google Scholar
• Jain, Saloni. 2015. “Real-Time Social Network Data Mining for Predicting the Path for a Disaster.” Georgia State University.
   Google Scholar
• Joachims, Thorsten. 1998. “Text Categorization with Support Vector Machines: Learning with Many Relevant Features.” In Machine Learning: ECML-98, 137–142, Berlin.
   Google Scholar
• Keim, Mark E, and Eric Noji. 2011. “Emergent Use of Social Media: A New Age of Opportunity for Disaster Resilience.” American Journal of Disaster Medicine 6 (1): 47–54.
   PubMedGoogle Scholar
• Kohavi, Ron, and Dan Sommerfield. 1995. “Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology.” Paper Presented at the Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), Montreal, Canada, August 20–21.
   Google Scholar
• Lakshmanan, Valliappa, and Travis Smith. 2009. “Data Mining Storm Attributes from Spatial Grids.” Journal of Atmospheric and Oceanic Technology 26 (11): 2353–2365.
   Web of Science ®Google Scholar
• Landwehr, Peter M., and Kathleen M. Carley. 2014. “Social Media in Disaster Relief.” In Data Mining and Knowledge Discovery for Big Data, edited by Wesley W. Chu, 225–257. Berlin: Springer.
   Google Scholar
• Li, Zhenlong, Cuizhen Wang, Christopher T. Emrich, and Diansheng 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.
   Web of Science ®Google Scholar
• Lindsay, Bruce R. 2011. “Social Media and Disasters: Current Uses, Future Options, and Policy Considerations.” In Congressional Research Service, Report for Congress, 1–13. Washington, DC.
   Google Scholar
• Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. “Distributed Representations of Words and Phrases and Their Compositionality.” Paper Presented at the Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, USA.
   Google Scholar
• Morstatter, Fred, Jürgen Pfeffer, Huan Liu, and Kathleen M. Carley. 2013. “Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose.” Paper Presented at the 7th International AAAI Conference on Weblogs and Social Media, Cambridge, Massachusetts, USA, June 28.
   Google Scholar
• NHC. 2017. “National Hurricane Center.” http://www.nhc.noaa.gov/.
   Google Scholar
• NWS. 2021. “Definitions of Weather Watch, Warnings and Advisories.” https://www.weather.gov/lwx/WarningsDefined.
   Google Scholar
• NWS Radar Scale. 2021. “The Front.” https://www.weather.gov/media/publications/front/06nov_Front.pdf.
   Google Scholar
• Peng, Bo, Qunying Huang, and Jinmeng Rao. 2021. “Spatiotemporal Contrastive Representation Learning for Building Damage Classification.” Paper Presented at the 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, July 11–16.
   Google Scholar
• Peng, Bo, Qunying Huang, Jamp Vongkusolkit, Song Gao, Daniel B. Wright, Zheng N. Fang, and Yi Qiang. 2020. “Urban Flood Mapping With Bitemporal Multispectral Imagery Via a Self-Supervised Learning Framework.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 2001–2016.
   Web of Science ®Google Scholar
• Pollard, Tom. 2021. “METAR.” https://pypi.python.org/pypi/metar.
   Google Scholar
• Rosser, Julian F., D. G. Leibovici, and M. J. Jackson. 2017. “Rapid Flood Inundation Mapping Using Social Media, Remote Sensing and Topographic Data.” Natural Hazards 87 (1): 103–120.
   Web of Science ®Google Scholar
• Roy, Chandan, and Rita Kovordányi. 2012. “Tropical Cyclone Track Forecasting Techniques – a Review.” Atmospheric Research 104: 40–69.
   Web of Science ®Google Scholar
• Sahare, Mahendra, and Hitesh Gupta. 2012. “A Review of Multi-Class Classification for Imbalanced Data.” International Journal of Advanced Computer Research 2 (3): 160.
   Google Scholar
• Schnebele, Emily, and Guido Cervone. 2013. “Improving Remote Sensing Flood Assessment Using Volunteered Geographical Data.” Natural Hazards and Earth System Sciences 13 (3): 669–677.
   Web of Science ®Google Scholar
• Shelton, Taylor, Ate Poorthuis, Mark Graham, and Matthew Zook. 2014. “Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’.” Geoforum 52: 167–179.
   Web of Science ®Google Scholar
• Spinsanti, Laura, and Frank Ostermann. 2013. “Automated Geographic Context Analysis for Volunteered Information.” Applied Geography 43: 36–44.
   Web of Science ®Google Scholar
• Sutton, Jeannette, Leysia Palen, and Irina Shklovski. 2008. “Backchannels on the Front Lines: Emergent Uses of Social Media in the 2007 Southern California Wildfires.” Paper Presented at the Proceedings of the 5th International ISCRAM Conference, Washington, DC, USA.
   Google Scholar
• Takahashi, Bruno, Edson C. Tandoc, Jr., and Christine Carmichael. 2015. “Communicating on Twitter During a Disaster: An Analysis of Tweets During Typhoon Haiyan in the Philippines.” Computers in Human Behavior 50: 392–398.
   Web of Science ®Google Scholar
• Tobler, Waldo R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46 (Sup1): 234–240.
   Web of Science ®Google Scholar
• Vieweg, Sarah, Amanda L. Hughes, Kate Starbird, and Leysia 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, Montréal Québec, April 22–27, 2006.
   Google Scholar
• Wang, Jimin, Yingjie Hu, and Kenneth Joseph. 2020. “NeuroTPR: A Neuro-Net Toponym Recognition Model for Extracting Locations from Social Media Messages.” Transactions in GIS 24 (3): 719–735.
   Web of Science ®Google Scholar
• Wang, Han, Erik Skau, Hamid Krim, and Guido Cervone. 2018. “Fusing Heterogeneous Data: A Case for Remote Sensing and Social Media.” IEEE Transactions on Geoscience and Remote Sensing 56 (12): 6956–6968.
   Web of Science ®Google Scholar
• Xiao, Yu, Qunying Huang, and Kai Wu. 2015. “Understanding Social Media Data for Disaster Management.” Natural Hazards 79 (3): 1663–1679. doi:https://doi.org/10.1007/s11069-015-1918-0.
   Web of Science ®Google Scholar
• Yang, Jingchao, Manzhu Yu, Han Qin, Mingyue Lu, and Chaowei Yang. 2019. “A Twitter Data Credibility Framework – Hurricane Harvey as a Use Case.” ISPRS International Journal of Geo-Information 8 (3): 111.
   Web of Science ®Google Scholar
• Yu, Manzhu, Qunying Huang, Han Qin, Chris Scheele, and Chaowei Yang. 2019. “Deep Learning for Real-Time Social Media Text Classification for Situation Awareness–Using Hurricanes Sandy, Harvey, and Irma as Case Studies.” International Journal of Digital Earth 12 (11): 1230–1247.
   Web of Science ®Google Scholar
• Zahra, Kiran, Muhammad Imran, and Frank O. Ostermann. 2020. “Automatic Identification of Eyewitness Messages on Twitter During Disasters.” Information Processing & Management 57 (1): 102107.
   Web of Science ®Google Scholar
• Zahra, Kiran, Frank O. Ostermann, and Ross S. Purves. 2017. “Geographic Variability of Twitter Usage Characteristics During Disaster Events.” Geo-Spatial Information Science 20 (3): 231–240.
   Web of Science ®Google Scholar
• Zhou, Sulong, Pengyu Kan, Qunying Huang, and Janet Silbernagel. 2021. “A Guided Latent Dirichlet Allocation Approach to Investigate Real-Time Latent Topics of Twitter Data During Hurricane Laura.” Journal of Information Science. doi:https://doi.org/10.1177/01655515211007724.
   Google Scholar
• Zou, Lei, Nina S. N. Lam, Heng Cai, and Yi Qiang. 2018. “Mining Twitter Data for Improved Understanding of Disaster Resilience.” Annals of the American Association of Geographers 108 (5): 1422–1441.
   Web of Science ®Google Scholar