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Classification of Tweet on Disaster Management Using Random Forest

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Advancements in Smart Computing and Information Security (ASCIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1759))

Abstract

The disaster management is highly responsible for managing the evacuation and deploying rescue teams to reduce the loss of lives and properties. However, it is considered challenging to obtain accurate information in timely fashion from various regions of the affected zones. With the advent of social media and networks, the information dissemination on such events can sense wide information from different zones but the information is in unstructured form. It is hence necessary to acquire correct or relevant information relating to that event. In this paper, we utilize random forest (RF) model to effectively classify the information from tweets (twitter.org) to find the location in case of a natural disaster. The proposed classification engine involves the collects of tweets, pre-processing of texts, RF classification and the extraction of location and determination. The classification is made effective using a pre-trained word vectors that includes the crisis words and global vectors for word representation (GLoVe). This pre-training captures the semantic meaning from the input tweets. Finally, extraction is performed to increase the accuracy of the model and in addition it determines the location of the disaster. The experiments are conducted on a real datasets from recent hurricanes. The results of simulation shows that the RF performs in a better way than other existing models in terms of accuracy, recall, precision and F1-score. It is seen that RF classifies effectively the tweets and analyses the accurate location.

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References

  1. Park, M., Sun, Y., McLaughlin, M.L.: Social media propagation of content promoting risky health behavior. Cyberpsychol. Behav. Soc. Netw. 20(5), 278–285 (2017)

    Article  Google Scholar 

  2. Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Quantifying controversy on social media. ACM Trans. Soc. Comput. 1(1), 1–27 (2018)

    Article  Google Scholar 

  3. Alexander, D.E.: Social media in disaster risk reduction and crisis management. Sci. Eng. Ethics 20(3), 717–733 (2014)

    Article  Google Scholar 

  4. Yang, Z., Nguyen, L.H., Stuve, J., Cao, G., Jin, F.: Harvey flooding rescue in social media. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 2177–2185. IEEE, December 2017

    Google Scholar 

  5. Shklovski, I., Burke, M., Kiesler, S., Kraut, R.: Technology adoption and use in the aftermath of Hurricane Katrina in New Orleans. Am. Behav. Sci. 53(8), 1228–1246 (2010)

    Article  Google Scholar 

  6. Baer, D.: As Sandy became# Sandy, emergency services got social. Fast Company, vol. 9 (2012)

    Google Scholar 

  7. Lindsay, B.R.: Social media and disasters: current uses, future options, and policy considerations (2011)

    Google Scholar 

  8. Ferrara, E., Wang, W.Q., Varol, O., Flammini, A., Galstyan, A.: Predicting Online Extremism, Content Adopters, and Interaction Reciprocity. International Conference on Social Informatics, pp. 22–39. Springer, New York (2016)

    Google Scholar 

  9. Azizan, S.A., Aziz, I.A.: Terrorism detection based on sentiment analysis using machine learning. J. Eng. Appl. Sci. 12(3), 691–698 (2017)

    Google Scholar 

  10. Hartung, M., Klinger, R., Schmidtke, F., Vogel, L.: Identifying right-wing extremism in German Twitter profiles: a classification approach. In: Frasincar, F., Ittoo, A., Nguyen, L.M., Métais, E. (eds.) NLDB 2017. LNCS, vol. 10260, pp. 320–325. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59569-6_40

    Chapter  Google Scholar 

  11. Nguyen, A., Hoang, Q., Nguyen, H., Nguyen, D., Tran, T.: Evaluating marijuana-related tweets on Twitter. In: IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1–7. IEEE, New Jersey (2017)

    Google Scholar 

  12. Ryan, S., Garth, D., Richard, F.: Searching for signs of extremism on the web: an introduction to sentiment-based identification of radical authors. Behav. Sci. Terror. Polit. Aggres. 10, 39–59 (2018)

    Article  Google Scholar 

  13. Chalothorn, T., Ellman, J.: Using SentiWordNet and sentiment analysis for detecting radical content on web forums (2012)

    Google Scholar 

  14. Bermingham, A., Conway, M., McInerney, L., O’Hare, N., Smeaton, A.F.: Combining social network analysis and sentiment analysis to explore the potential for online radicalisation. In: IEEE International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009. pp. 231–236 (2009)

    Google Scholar 

  15. Zahera, H.M., Jalota, R., Sherif, M.A., Ngomo, A.N.: I-AID: identifying actionable information from disaster-related tweets. arXiv preprint arXiv:2008.13544 (2020)

  16. Garvey, W.T., Mechanick, J.I.: Medically actionable disease classification system for obesity. Obesity (Silver Spring, Md.) 28(7), 1169 (2020)

    Google Scholar 

  17. Garvey, W.T., Mechanick, J.I.: Proposal for a scientifically correct and medically actionable disease classification system (ICD) for obesity. Obesity 28(3), 484–492 (2020)

    Article  Google Scholar 

  18. Tzacheva, A.A., Ranganathan, J., Bagavathi, A.: Action rules for sentiment analysis using Twitter. Int. J. Soc. Netw. Mining 3(1), 35–51 (2020)

    Article  Google Scholar 

  19. Kruspe, A., Kersten, J., Klan, F.: Detection of actionable tweets in crisis events. Nat. Hazard. 21(6), 1825–1845 (2021)

    Article  Google Scholar 

  20. Roy, S.S., Dey, S., Chatterjee, S.: Autocorrelation aided random forest classifier-based bearing fault detection framework. IEEE Sens. J. 20(18), 10792–10800 (2020)

    Article  Google Scholar 

  21. Adams, G., Ketenci, M., Bhave, S., Perotte, A., Elhadad, N.: Zero-shot clinical acronym expansion via latent meaning cells. In: Machine Learning for Health, pp. 12–40. PMLR, November 2020

    Google Scholar 

  22. Miller, M., Romine, W.L.: Anthrax event detection using Twitter: analysis of unigram and bigrams for relevant vs non-relevant tweets (2020)

    Google Scholar 

  23. HaCohen-Kerner, Y., Miller, D., Yigal, Y.: The influence of preprocessing on text classification using a bag-of-words representation. PLoS ONE 15(5), e0232525 (2020)

    Article  Google Scholar 

  24. Duan, Z., et al.: Towards cleaner wastewater treatment for special removal of cationic organic dye pollutants: a case study on application of supramolecular inclusion technology with β-cyclodextrin derivatives. J. Clean. Prod. 256, 120308 (2020)

    Article  Google Scholar 

  25. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543, October 2014

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, SRM Institute of Science and Technology (Ramapuram Campus), Chennai, Tamilnadu, India

    T. Kanimozhi & S. Belina V J Sara

Authors
  1. T. Kanimozhi
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  2. S. Belina V J Sara
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Correspondence to T. Kanimozhi .

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Editors and Affiliations

  1. Marwadi University, Rajkot, India

    Sridaran Rajagopal

  2. AVPTI, Rajkot, India

    Parvez Faruki

  3. Marwadi University, Rajkot, India

    Kalpesh Popat

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Kanimozhi, T., Belina V J Sara, S. (2022). Classification of Tweet on Disaster Management Using Random Forest. In: Rajagopal, S., Faruki, P., Popat, K. (eds) Advancements in Smart Computing and Information Security. ASCIS 2022. Communications in Computer and Information Science, vol 1759. Springer, Cham. https://doi.org/10.1007/978-3-031-23092-9_15

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  • DOI: https://doi.org/10.1007/978-3-031-23092-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23091-2

  • Online ISBN: 978-3-031-23092-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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