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Classification of crisis-related data on Twitter using a deep learning-based framework

  • 1209: Recent Advances on Social Media Analytics and Multimedia Systems: Issues and Challenges
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Abstract

In recent years, many citizens use social media platforms like Twitter to share and get the most up-to-date information regarding crisis events such as natural and man-made crisis. Automatically identifying this crisis-related information from social media data is a challenging task because of the sheer amount of data is being communicated between users during such crisis situations. The general public, response groups, and relief agencies can increase situational awareness by identifying and assessing crisis-related information from these massive amounts of social media data in real-time. Many studies have been published, that employ traditional machine learning approaches to detect crisis events, as well as others that use a deep neural network. In recent years, the models based on the deep neural network have outperformed traditional machine learning models for a variety of tasks. Two popularly used deep neural network models are Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU). The local features can be detected by CNN in a multidimensional field, while the GRU network can learn sequential data because it can remember previously read data. In this paper, we propose two novel hybrid deep neural network models. The first model combines CNN and GRU and the second one combines CNN with SkipCNN. We evaluate our proposed models on 4 different datasets provided by CrisisNLP to show their effectiveness in detecting crisis-related information as well as identifying different types of information required for humanitarian aid. We find that from our proposed models CNN-SkipCNN is the best performing model and achieving better results than the state-of-the-art methods with an improvement of up to 16.55 absolute points for detecting crisis-related events and with an improvement of up to 21.71 absolute points in detecting different types of crisis information.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, IIIT Bhubaneswar, Bhubaneswar, Odisha, India

    Nayan Ranjan Paul & Rakesh Chandra Balabantaray

  2. Department of Faculty of Emerging Technologies, Sri Sri University, Cuttack, Odisha, India

    Deepak Sahoo

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  1. Nayan Ranjan Paul
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  3. Rakesh Chandra Balabantaray
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Correspondence to Nayan Ranjan Paul.

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Paul, N.R., Sahoo, D. & Balabantaray, R.C. Classification of crisis-related data on Twitter using a deep learning-based framework. Multimed Tools Appl 82, 8921–8941 (2023). https://doi.org/10.1007/s11042-022-12183-w

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  • DOI: https://doi.org/10.1007/s11042-022-12183-w

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