Skip to main content
SpringerLink
Log in

Randomized Convolutional Neural Network Architecture for Eyewitness Tweet Identification During Disaster

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

During a disaster, Twitter is flooded with disaster-related information. Among huge disaster-related Twitter posts, a fraction of them is posted by the eyewitness of disaster. The post of an eyewitness of the disaster contains an authentic description of the disaster. Therefore, eyewitness disaster-related posts are preferred over all other sources of information to know the floor reality of the disaster. In this work, we have used a convolutional neural network (CNN) with randomly initialized weights to extract features from the textual contents of the tweets and proposed three different random neural network-based models. The feature extracted from the untrained random convolutional neural network (RCNN) is passed through a trainable dense neural network (DNN), echo state network (ESN), and extreme learning machine (ELM) to identify eyewitness tweets. The proposed system is validated with hurricane, earthquake, flood, and wildfire datasets. In the extensive experiments with three different random neural network-based models such as RCNN-DNN, RCNN-ESN, RCNN-ELM, and other machine learning and deep learning models such as KNN, Naive Bayes, Decision Tree, Convolutional neural network, and Dense Neural Network, the RCNN-DNN model outperformed all the other models. The RCNN-DNN model achieved impressive performance with a weighted F1-scores of 0.79, 0.86, 0.79, and 0.85 for hurricane, earthquake, flood, and wildfire, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Al Khushi, N., Côté, A.: Apparent life-threatening events: assessment, risks, reality. Paediatr. Respir. Rev. 12(2), 124–132 (2011)

    Article  Google Scholar 

  2. Bianchi, F.M., Scardapane, S., Løkse, S, Jenssen, R.: Reservoir computing approaches for representation and classification of multivariate time series. IEEE Transactions on Neural Networks and Learning Systems, 1–11. https://doi.org/10.1109/TNNLS.2020.3001377 (2020)

  3. Cao, J., Zhang, K., Luo, M., Yin, C., Lai, X.: Extreme learning machine and adaptive sparse representation for image classification. Neural Netw. 81, 91–102 (2016)

    Article  Google Scholar 

  4. Chang, H., Futagami, K.: Convolutional reservoir computing for world models. arXiv:1907.08040 (2019)

  5. Darabian, H., Homayounoot, S., Dehghantanha, A., Hashemi, S., Karimipour, H., Parizi, R.M., Choo, K.K.R.: Detecting cryptomining malware: a deep learning approach for static and dynamic analysis. J. Grid. Comput., 1–11 (2020)

  6. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, 233–240 (2006)

  7. Doggett, E., Cantarero, A.: Identifying eyewitness news-worthy events on twitter. In: Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media, 7–13 (2016)

  8. Fang, R., Nourbakhsh, A., Liu, X., Shah, S., Li, Q.: Witness identification in twitter. In: Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media, 65–73 (2016)

  9. Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345–420 (2016)

    Article  MathSciNet  Google Scholar 

  10. Gupta, A., Sahu, H., Nanecha, N., Kumar, P., Roy, P.P., Chang, V.: Enhancing text using emotion detected from eeg signals. J. Grid. Comput. 17(2), 325–340 (2019)

    Article  Google Scholar 

  11. Huang, C.F.: Evaluation of system reliability for a stochastic delivery-flow distribution network with inventory. Ann. Oper. Res. 277(1), 33–45 (2019)

    Article  MathSciNet  Google Scholar 

  12. Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics) 42(2), 513–529 (2011)

    Article  Google Scholar 

  13. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1-3), 489–501 (2006)

    Article  Google Scholar 

  14. Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey. ACM Computing Surveys (CSUR) 47(4), 1–38 (2015)

    Article  Google Scholar 

  15. Imran, M., Ofli, F., Caragea, D., Torralba, A.: Using ai and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions Information Processing & Management 57(5). https://doi.org/10.1016/j.ipm.2020.102261 (2020)

  16. Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany:, German National Research Center for Information Technology GMD Technical Report 148(34), 13 (2001)

    Google Scholar 

  17. Jayawardene, I., Venayagamoorthy, G.K.: Comparison of Echo State Network and Extreme Learning Machine for Pv Power Prediction. In: 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), 1–8 (2014)

  18. Katuwal, R., Suganthan, P.N., Tanveer, M.: Random vector functional link neural network based ensemble deep learning. arXiv:1907.00350 (2019)

  19. Katuwal, R., Suganthan, P.N., Zhang, L.: An ensemble of decision trees with random vector functional link networks for multi-class classification. Appl. Soft Comput. 70, 1146–1153 (2018)

    Article  Google Scholar 

  20. Kingma, D.P., Ba, J.: Adam:, A method for stochastic optimization. arXiv:1412.6980 (2014)

  21. Kumar, A., Singh, J.P.: Location reference identification from tweets during emergencies: a deep learning approach. Int. J. Disaster Risk Reduction 33, 365–375 (2019)

    Article  Google Scholar 

  22. Kumar, A., Singh, J.P., Dwivedi, Y.K., Rana, N.P.: A deep multi-modal neural network for informative twitter content classification during emergencies. Annals of Operations Research 1–32. https://doi.org/10.1007/s10479-020-03514-x (2020)

  23. Kumar, A., Singh, J.P., Saumya, S.: A Comparative Analysis of Machine Learning Techniques for Disaster-Related Tweet Classification IEEE Region 10 Humanitarian Technology Conference, 222–227 (2019)

  24. Li, D., Han, M., Wang, J.: Chaotic time series prediction based on a novel robust echo state network. IEEE Transactions on Neural Networks and Learning Systems 23(5), 787–799 (2012)

    Article  Google Scholar 

  25. Liu, D., Chen, L., Wang, Z., Diao, G.: Speech expression multimodal emotion recognition based on deep belief network. J. Grid. Comput. 19(2), 1–13 (2021)

    Article  Google Scholar 

  26. Loyola-González, O., Medina-Pérez, M.A., Choo, K.K.R.: A review of supervised classification based on contrast patterns: Applications, trends, and challenges. J. Grid. Comput., 1–49 (2020)

  27. Lukoševičius, M.: A Practical guide to applying echo state networks, 659–686. Springer, Berlin (2012)

    Google Scholar 

  28. Ma, Q., Shen, L., Chen, W., Wang, J., Wei, J., Yu, Z.: Functional echo state network for time series classification. Inform. Sci. 373, 1–20 (2016)

    Article  Google Scholar 

  29. Morstatter, F., Lubold, N., Pon-Barry, H., Pfeffer, J., Liu, H.: Finding eyewitness tweets during crises. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, 23-27. Association for Computational Linguistics, Baltimore, MD, USA. https://doi.org/10.3115/v1/W14-2509 (2014)

  30. Nilsang, S., Yuangyai, C., Cheng, C.Y., Janjarassuk, U.: Locating an ambulance base by using social media: a case study in bangkok. Ann. Oper. Res. 283(1), 497–516 (2019)

    Article  MathSciNet  Google Scholar 

  31. Pekar, V., Binner, J., Najafi, H., Hale, C., Schmidt, V.: Early detection of heterogeneous disaster events using social media. J. Assoc. Inf. Sci. Technol. 71(1), 43–54 (2020)

    Article  Google Scholar 

  32. Qiu, X., Suganthan, P.N., Amaratunga, G.A.: Ensemble incremental learning random vector functional link network for short-term electric load forecasting. Knowl.-Based Syst. 145, 182–196 (2018)

    Article  Google Scholar 

  33. Qu, B.Y., Lang, B., Liang, J.J., Qin, A.K., Crisalle, O.D.: Two-hidden-layer extreme learning machine for regression and classification. Neurocomputing 175, 826–834 (2016)

    Article  Google Scholar 

  34. Saeed, Z., Abbasi, R.A., Maqbool, O., Sadaf, A., Razzak, I., Daud, A., Aljohani, N.R., Xu, G.: What’s happening around the world? a survey and framework on event detection techniques on twitter. J. Grid. Comput. 17(2), 279–312 (2019)

    Article  Google Scholar 

  35. Singh, J.P., Dwivedi, Y.K., Rana, N.P., Kumar, A., Kapoor, K.K.: Event classification and location prediction from tweets during disasters. Ann. Oper. Res. 283(1), 737–757 (2019)

    Article  Google Scholar 

  36. Stefan, I., Rebedea, T., Caragea, D.: Classification of Eyewitness Tweets in Emergency Situations. In: RoCHI, 46–52 (2019)

  37. Subasi, A., Khateeb, K., Brahimi, T., Sarirete, A.: Human Activity Recognition Using Machine Learning Methods in a Smart Healthcare Environment. In: Innovation in Health Informatics, 123–144. Elsevier (2020)

  38. Tanev, H., Zavarella, V., Steinberger, J.: Monitoring Disaster Impact: Detecting Micro-Events and Eyewitness Reports in Mainstream and Social Media. In: ISCRAM (2017)

  39. Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2015)

    Article  MathSciNet  Google Scholar 

  40. Tanisaro, P., Heidemann, G.: Time series classification using time warping invariant echo state networks. In: 2016 15Th IEEE International Conference on Machine Learning and Applications (ICMLA), 831–836. IEEE (2016)

  41. Timotheou, S.: The random neural network: a survey. Comput. J. 53(3), 251–267 (2010)

    Article  Google Scholar 

  42. Tong, M.H., Bickett, A.D., Christiansen, E.M., Cottrell, G.W.: Learning grammatical structure with echo state networks. Neural Netw. 20(3), 424–432 (2007)

    Article  Google Scholar 

  43. Tong, Z., Tanaka, G.: Reservoir Computing with Untrained Convolutional Neural Networks for Image Recognition. In: 2018 24Th International Conference on Pattern Recognition (ICPR), 1289–1294. IEEE (2018)

  44. Truelove, M., Khoshelham, K., McLean, S., Winter, S., Vasardani, M.: Identifying witness accounts from social media using imagery. ISPRS International Journal of Geo-Information 6(4), 120 (2017)

    Article  Google Scholar 

  45. Truelove, M., Vasardani, M., Winter, S.: Towards credibility of micro-blogs: characterising witness accounts. GeoJournal 80(3), 339–359 (2015)

    Article  Google Scholar 

  46. Wamba, S.F., Edwards, A., Akter, S.: Social media adoption and use for improved emergency services operations: the case of the nsw ses. Ann. Oper. Res. 283(1–2), 225–245 (2019)

    Article  Google Scholar 

  47. Yin, Y.: Deep learning with the random neural network and its applications. arXiv:1810.08653 (2018)

  48. Yin, Y.: Random neural network methods and deep learning. Probability in the Engineering and Informational Sciences, 1–31. https://doi.org/10.1017/S026996481800058X (2019)

  49. Zahra, K., Imran, M., Ostermann, F.O.: Automatic identification of eyewitness messages on twitter during disasters. Inf. Process. Manag. 57(1), 102107 (2020)

    Article  Google Scholar 

  50. Zahra, K., Imran, M., Ostermann, F.O., Boersma, K., Tomaszewski, B.: Understanding eyewitness reports on twitter during disasters. In: Proceedings of the of the ISCRAM (2018) (2018)

  51. Zhang, L., Suganthan, P.N.: A comprehensive evaluation of random vector functional link networks. Inf. Sci. 367, 1094–1105 (2016)

    Article  Google Scholar 

  52. Zhang, L., Suganthan, P.N.: A survey of randomized algorithms for training neural networks. Infor. Sci. 364, 146–155 (2016)

    Article  Google Scholar 

  53. Zola, P., Ragno, C., Cortez, P.: A google trends spatial clustering approach for a worldwide twitter user geolocation. Inf. Process. Manag. 57(6), 102312 (2020). https://doi.org/10.1016/j.ipm.2020.102312

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

    Abhinav Kumar

  2. Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India

    Jyoti Prakash Singh & Amit Kumar Singh

Authors
  1. Abhinav Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jyoti Prakash Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhinav Kumar.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, A., Singh, J.P. & Singh, A.K. Randomized Convolutional Neural Network Architecture for Eyewitness Tweet Identification During Disaster. J Grid Computing 20, 20 (2022). https://doi.org/10.1007/s10723-022-09609-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-022-09609-y

Keywords

Search

Navigation