ABSTRACT
Disaster monitoring based on social media posts has raised a lot of interest in the domain of computer science the last decade, mainly due to the wide area of applications in public safety and security and due to the pervasiveness not solely on daily communication but also in life-threating situations. Social media can be used as a valuable source for producing early warnings of eminent disasters. This paper presents a framework to analyse social media multimodal content, in order to decide if the content is relevant to flooding. This is very important since it enhances the crisis situational awareness and supports various crisis management procedures such as preparedness. Evaluation on a benchmark dataset shows very good performance in both text and image classification modules.
- Flavia Sofia Acerbo and Claudio Rossi. 2017. Filtering Informative Tweets During Emergencies: A Machine Learning Approach Proceedings of the First CoNEXT Workshop on ICT Tools for Emergency Networks and DisastEr Relief (I-TENDER '17). ACM, New York, NY, USA, 1--6. Google ScholarDigital Library
- Charu C Aggarwal and ChengXiang Zhai. 2012. Mining text data. Springer Science & Business Media. Google ScholarDigital Library
- Leo Breiman. 2001. Random forests. Machine learning, Vol. 45, 1 (2001), 5--32. Google ScholarDigital Library
- Joachim Daiber, Max Jakob, Chris Hokamp, and Pablo N Mendes. 2013. Improving efficiency and accuracy in multilingual entity extraction Proceedings of the 9th International Conference on Semantic Systems. ACM, 121--124. Google ScholarDigital Library
- Muhammad Imran, Carlos Castillo, Fernando Diaz, and Sarah Vieweg. 2015. Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR) Vol. 47, 4 (2015), 67. Google ScholarDigital Library
- Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding Proceedings of the 22nd ACM international conference on Multimedia. ACM, 675--678. Google ScholarDigital Library
- Vijaymeena M K and Kavitha K. 2016. A Survey on Similarity Measures in Text Mining. Vol. 3 (03. 2016), 19--28.Google Scholar
- Foteini Markatopoulou, Vasileios Mezaris, and Ioannis Patras. 2015. Cascade of classifiers based on binary, non-binary and deep convolutional network descriptors for video concept detection. In Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 1786--1790.Google ScholarDigital Library
- Raina M Merchant, Stacy Elmer, and Nicole Lurie. 2011. Integrating social media into emergency-preparedness efforts. New England Journal of Medicine Vol. 365, 4 (2011), 289--291.Google ScholarCross Ref
- Stuart E Middleton, Lee Middleton, and Stefano Modafferi. 2014. Real-time crisis mapping of natural disasters using social media. IEEE Intelligent Systems Vol. 29, 2 (2014), 9--17.Google ScholarCross Ref
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119. Google ScholarDigital Library
- Anastasia Moumtzidou, Symeon Papadopoulos, Stefanos Vrochidis, Ioannis Kompatsiaris, Konstantinos Kourtidis, George Hloupis, Ilias Stavrakas, Konstantina Papachristopoulou, and Christodoulos Keratidis. 2016. Towards Air Quality Estimation Using Collected Multimodal Environmental Data International Workshop on the Internet for Financial Collective Awareness and Intelligence. Springer, 147--156.Google Scholar
- Nikiforos Pittaras, Foteini Markatopoulou, Vasileios Mezaris, and Ioannis Patras. 2017. Comparison of fine-tuning and extension strategies for deep convolutional neural networks International Conference on Multimedia Modeling. Springer, 102--114.Google Scholar
- MV Sangameswar, M Nagabhushana Rao, and S Satyanarayana. 2017. An algorithm for identification of natural disaster affected area. Journal of Big Data, Vol. 4, 1 (2017), 39.Google ScholarCross Ref
- Hitoshi Sato, Kunihiro Takeda, Kazuhiro Matsumoto, Hirokazu Anai, and Yuzuru Yamakage. 2016. Efforts for Disaster Prevention/Mitigation to Protect Society from Major Natural Disasters. FUJITSU Sci. Tech. J, Vol. 52, 1 (2016), 107--113.Google Scholar
- P Selvaperumal and A Suruliandi. 2014. A short message classification algorithm for tweet classification Recent Trends in Information Technology (ICRTIT), 2014 International Conference on. IEEE, 1--3.Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR Vol. abs/1409.1556 (2014). {arxiv}1409.1556 http://arxiv.org/abs/1409.1556Google Scholar
- Ge Song, Yunming Ye, Xiaolin Du, Xiaohui Huang, and Shifu Bie. 2014. Short Text Classification: A Survey. Journal of Multimedia, Vol. 9, 5 (2014).Google ScholarCross Ref
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1--9.Google ScholarCross Ref
- Nataliya Tkachenko, Stephen Jarvis, and Rob Procter. 2017. Predicting floods with Flickr tags. PloS one, Vol. 12, 2 (2017), e0172870.Google ScholarCross Ref
- Brandon Truong, Cornelia Caragea, Anna Squicciarini, and Andrea H Tapia. 2014. Identifying valuable information from twitter during natural disasters. Proceedings of the Association for Information Science and Technology, Vol. 51, 1 (2014), 1--4.Google ScholarCross Ref
- Si Si Mar Win and Than Nwe Aung. 2017. Target oriented tweets monitoring system during natural disasters Computer and Information Science (ICIS), 2017 IEEE/ACIS 16th International Conference on. IEEE, 143--148.Google Scholar
- Jun Yan. 2009. Text Representation. Springer US, Boston, MA, 3069--3072.Google Scholar
- Jie Yin, Andrew Lampert, Mark Cameron, Bella Robinson, and Robert Power. 2012. Using social media to enhance emergency situation awareness. IEEE Intelligent Systems Vol. 27, 6 (2012), 52--59. Google ScholarDigital Library
Index Terms
- Flood Relevance Estimation from Visual and Textual Content in Social Media Streams
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