Title: SMedia: social media data analysis for emergency detection and its type identification

Authors: Sarmistha Nanda; Chhabi Rani Panigrahi; Bibudhendu Pati; Prasant Mohapatra

Addresses: Department of Computer Science, Rama Devi Women's University, India; Department of Computer Science, UC Davis, Davis, CA 95616, USA ' Department of Computer Science, Rama Devi Women's University, India; Department of Computer Science, UC Davis, Davis, CA 95616, USA ' Department of Computer Science, Rama Devi Women's University, India; Department of Computer Science, UC Davis, Davis, CA 95616, USA ' Department of Computer Science, Rama Devi Women's University, India; Department of Computer Science, UC Davis, Davis, CA 95616, USA

Abstract: Due to the advancement of technology, social media can spread information very fast. People post information about themselves or about an event in the proximity of any emergency. However, proper analysis of social media data is necessary to address the challenges of emergency detection and its type identification. An early identification along with proper action is essential to minimise the loss due to occurrence of any type of emergency. In this work, authors used the keyword based tweets data to detect the emergency. First, the emergency tweets were classified using the proposed HDLed model and the accuracy obtained from the experimental study was 88% which was more as compared to the existing algorithms such as convolutional neural network (CNN), bidirectional-long short-term memory (Bi-LSTM), long short-term memory (LSTM), and gated recurrent unit (GRU). Next, the type of emergency was detected using the baseline multiclass classifiers such as naïve Bayes (NB), decision tree (DT), stochastic gradient descent (SGD), and random forest (RF) and it was found that SGD gives an accuracy of 92% which was better as compared to other considered baseline algorithms.

Keywords: social media; text classification; convolutional neural network; CNN; bidirectional LSTM; Bi-LSTM; emergency; short-term memory; LSTM; gated recurrent unit; GRU; stochastic gradient descent; SGD.

DOI: 10.1504/IJCSE.2023.132178

International Journal of Computational Science and Engineering, 2023 Vol.26 No.4, pp.385 - 396

Received: 16 Aug 2022
Received in revised form: 22 Jan 2023
Accepted: 25 Jan 2023
Published online: 12 Jul 2023 *

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