Abstract
During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models.
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Alam, F., Imran, M., & Ofli, F. (2017). Image4act: Online social media image processing for disaster response. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 601–604). ACM.
Bandyopadhyay, A., Ganguly, D., Mitra, M., Saha, S.K., & Jones, G.J. (2018). An embedding based ir model for disaster situations. Information Systems Frontiers, 20, 925–932.
Beydoun, G., Dascalu, S., Dominey-Howes, D., & Sheehan, A. (2018). Disaster management and information systems: Insights to emerging challenges. Information Systems Frontiers, 20, 649–652.
Caragea, C., Silvescu, A., & Tapia, A.H. (2016). Identifying informative messages in disaster events using convolutional neural networks. In International conference on information systems for crisis response and management (pp. 137–147).
Chaudhuri, N., & Bose, I. (2019). Application of image analytics for disaster response in smart cities. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555
Daly, S., & Thom, J.A. (2016). Mining and classifying image posts on social media to analyse fires. In ISCRAM (pp. 1–14). Citeseer.
Doggett, E., & Cantarero, A. (2016). Identifying eyewitness news-worthy events on Twitter. In Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media (pp. 7–13).
Dutt, R., Hiware, K., Ghosh, A., & Bhaskaran, R. (2018). Savitr: a system for real-time location extraction from microblogs during emergencies. In Companion Proceedings of the The Web Conference, (Vol. 2018 pp. 1643–1649).
Fang, R., Nourbakhsh, A., Liu, X., Shah, S., & Li, Q. (2016). Witness identification in Twitter. In Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media (pp. 65–73).
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: a survey. ACM Computing Surveys (CSUR), 47, 1–38.
Imran, M., Castillo, C., Lucas, J., Meier, P., & Vieweg, S. (2014). Aidr: Artificial intelligence for disaster response. In Proceedings of the 23rd International Conference on World Wide Web (pp. 159–162).
Imran, M., Ofli, F., Caragea, D., & Torralba, A. (2020). Using AI and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions. Information Processing & Management 57. https://doi.org/10.1016/j.ipm.2020.102261
Kersten, J., Kruspe, A., Wiegmann, M., & Klan, F. (2019). Robust Filtering of crisis-related tweets. In Z. Franco J.H. Canós (Eds.) ISCRAM. https://elib.dlr.de/127586/
Kingma, D.P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980
Kohavi, R., et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, (Vol. 14 pp. 1137–1145). Montreal: Canada.
Kumar, A., & Singh, J.P. (2019). Location reference identification from tweets during emergencies: a deep learning approach. International Journal of Disaster Risk Reduction, 33, 365–375.
Kumar, A., Singh, J.P., Dwivedi, Y.K., & Rana, N.P. (2020). A deep multi-modal neural network for informative Twitter content classification during emergencies. Annals of Operations Research, pp. 1–32. https://doi.org/10.1007/s10479-020-03514-x
Kumar, A., Singh, J.P., & Rana, N.P. (2017). Authenticity of geo-location and place name in tweets. In Proceedings of the 23rd Americas Conference on Information Systems (pp. 1–10).
Kumar, A., Singh, J.P., & Saumya, S. (2019). A comparative analysis of machine learning techniques for disaster-related tweet classification. In IEEE Region 10 Humanitarian Technology Conference (pp. 222–227).
Kumar, S., Morstatter, F., Zafarani, R., & Liu, H. (2013). Whom should I follow? Identifying relevant users during crises. In Proceedings of the 24th ACM conference on hypertext and social media (pp. 139–147).
Lagerstrom, R., Arzhaeva, Y., Szul, P., Obst, O., Power, R., Robinson, B., & Bednarz, T. (2016). Image classification to support emergency situation awareness. Frontiers in Robotics and AI, 3, 54.
Levy, O., & Goldberg, Y. (2014). Dependency-based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, (Vol. 2, Short Papers pp. 302–308).
Liu, F., & Xu, D. (2018). Social roles and consequences in using social media in disasters: a structurational perspective. Information Systems Frontiers, 20, 693–711.
Madichetty, S., & Sridevi, M. (2019). Detecting informative tweets during disaster using deep neural networks. In 2019 11th International Conference on Communication Systems & Networks (COMSNETS) (pp. 709–713). IEEE.
Madichetty, S., & Sridevi, M. (2021). A novel method for identifying the damage assessment tweets during disaster. Future Generation Computer Systems, 116, 440–454.
Mendon, S., Dutta, P., Behl, A., & Lessmann, S. (2021). A hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from Twitter data of natural disasters. Information Systems Frontiers, pp. 1–24.
Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., & Khudanpur, S. (2010). Recurrent neural network-based language model. In Interspeech, Makuhari, (Vol. 2 pp. 1045–1048).
Mirbabaie, M., Ehnis, C., Stieglitz, S., Bunker, D., & Rose, T. (2021). Digital nudging in social media disaster communication. Information Systems Frontiers, 23, 1097–1113.
Mohanty, S.D., Biggers, B., Sayedahmed, S., Pourebrahim, N., Goldstein, E.B., Bunch, R., Chi, G., Sadri, F., McCoy, T.P., & Cosby, A. (2021). A multi-modal approach towards mining social media data during natural disasters - a case study of hurricane irma. International Journal of Disaster Risk Reduction, 54, 102032.
Morstatter, F., Lubold, N., Pon-Barry, H., Pfeffer, J., & Liu, H. (2014). Finding eyewitness tweets during crises. In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science. Baltimore, MD, USA: Association for Computational Linguistics.
Nguyen, D., Al Mannai, K.A., Joty, S., Sajjad, H., Imran, M., & Mitra, P. (2017a). Robust classification of crisis-related data on social networks using convolutional neural networks. In Proceedings of the International AAAI Conference on Web and Social Media, (Vol. 11 pp. 632–635).
Nguyen, D.T., Alam, F., Ofli, F., & Imran, M. (a). Automatic image filtering on social networks using deep learning and perceptual hashing during crises. arXiv:1704.02602
Nguyen, D.T., Joty, S., Imran, M., Sajjad, H., & Mitra, P. (b). Applications of online deep learning for crisis response using social media information. arXiv:1610.01030
Nguyen, D.T., Ofli, F., Imran, M., & Mitra, P. (2017b). Damage assessment from social media imagery data during disasters. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (Vol. 2017 pp. 569–576).
Olteanu, A., Vieweg, S., & Castillo, C. (2015). What to expect when the unexpected happens: Social media communications across crises. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 994–1009).
Palshikar, G.K., Apte, M., & Pandita, D. (2018). Weakly supervised and online learning of word models for classification to detect disaster reporting tweets. Information Systems Frontiers, 20, 949–959.
Pekar, V., Binner, J., Najafi, H., Hale, C., & Schmidt, V. (2020). Early detection of heterogeneous disaster events using social media. Journal of the Association for Information Science and Technology, 71, 43–54.
Pham, D.-H., & Le, A.-C. (2018). Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis. International Journal of Approximate Reasoning, 103, 1–10.
Powers, D.M.W. (2011). Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. https://doi.org/10.48550/ARXIV.2010.16061
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020). Pre-trained models for natural language processing: a survey. Science China Technological Sciences, 63, 1872–1897.
Roy, S., Mishra, S., & Matam, R. (2020). Classification and summarization for informative tweets. In 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1–4). IEEE.
Sakaki, T., Okazaki, M., & Matsuo, Y. (2012). Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering, 25, 919–931.
Singh, J.P., Dwivedi, Y.K., Rana, N.P., Kumar, A., & Kapoor, K.K. (2019). Event classification and location prediction from tweets during disasters. Annals of Operations Research, 283, 737–757. https://doi.org/10.1007/s10479-017-2522-3
Stefan, I., Rebedea, T., & Caragea, D. (2019). Classification of eyewitness tweets in emergency situations. In RoCHI (pp. 46–52).
Tanev, H., Zavarella, V., & Steinberger, J. (2017). Monitoring disaster impact: detecting micro-events and eyewitness reports in mainstream and social media. In ISCRAM.
Truelove, M., Khoshelham, K., McLean, S., Winter, S., & Vasardani, M. (2017). Identifying witness accounts from social media using imagery. ISPRS International Journal of Geo-Information, 6, 120.
Truelove, M., Vasardani, M., & Winter, S. (2015). Towards credibility of micro-blogs: characterising witness accounts. GeoJournal, 80, 339–359.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008).
Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016). Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 606–615).
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the Association for Computational Linguistics: human language technologies (pp. 1480–1489).
Yu, M., Huang, Q., Qin, H., Scheele, C., & Yang, C. (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, 0, 1–18. https://doi.org/10.1080/17538947.2019.1574316
Zahra, K., Imran, M., & Ostermann, F.O. (2020). Automatic identification of eyewitness messages on Twitter during disasters. Information Processing & Management, 57, 102107.
Zahra, K., Imran, M., Ostermann, F.O., Boersma, K., & Tomaszewski, B. (2018). Understanding eyewitness reports on Twitter during disasters. In Proceedings of the of the ISCRAM, (Vol. 2018 pp. 687–695).
Zhang, Y., Roller, S., & Wallace, B.C. (2016). MGNC-CNN: A simple approach to exploiting multiple word embeddings for sentence classification. In Proceedings of NAACL-HLT (pp. 1522–1527).
Zola, P., Ragno, C., & Cortez, P.A. (2020). Google Trends spatial clustering approach for a worldwide Twitter user geolocation. Information Processing & Management, 57, 102312.
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Kumar, A., Singh, J.P., Rana, N.P. et al. Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster. Inf Syst Front 25, 1589–1604 (2023). https://doi.org/10.1007/s10796-022-10309-x
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DOI: https://doi.org/10.1007/s10796-022-10309-x