An Autoregressive Transformer Model For Crisis Management Using Twitter Tweets

Authors

  • JULANTA LEELA RACHEL J
  • BHUVANESWARI A

DOI:

https://doi.org/10.59670/ml.v20i7.7916

Abstract

A disaster is a major event that lasts for a long time and causes a lot of damage to people, property, the economy, or the environment. The damage is so bad that the group or society that is affected can't handle it on its own. When a disaster happens, it costs developing countries the most. More than 95% of all deaths from hazards happen in developing countries, and as a share of gross domestic product, losses from natural hazards are 20 times higher in developing countries than in developed nations. To minimize the loss at the time of disaster and to overcome it quickly, a survey is taken on how to manage disaster effectively using Machine Learning and social networks. Social media platforms like Twitter, Instagram and Facebook acts as a communication medium during disaster and also help to take preventive measures before the occurrence of the disaster. Machine learning algorithms also helps in response and recovery phase after the occurrence of the disaster. The paper aims to provides an elaborated review on the concepts of Machine Learning (ML) along with social media and addresses how various other technologies like use of physical sensors, Remote sensors, Graphical Information System (GIS), Internet of Things (IoT) and Neural Network[1]s like Artificial Neural Network (ANN), Deep Neural Network (DNN) can combine with Machine Learning algorithms for the management of disaster efficiently using social media. Finally, based on the study, a generalized autoregressive pre-training method called XLNet is proposed which overcomes the limitations of BERT using its autoregressive formulation and directions for the future research is explained further.

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Published

2024-02-17

How to Cite

J , J. L. R. ., & A, B. . (2024). An Autoregressive Transformer Model For Crisis Management Using Twitter Tweets. Migration Letters, 21(S6), 323–339. https://doi.org/10.59670/ml.v20i7.7916

Issue

Section

Articles