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This paper aims to propose DisDSS: a Web-based smart disaster management (DM) system for decision-making that will assist disaster professionals in determining the nature of disaster-related social media (SM) messages. The research classifies the tweets into need-based, availability-based, situational-based, general and irrelevant categories and visualizes them on a web interface, location-wise.

It is worth mentioning that a fusion-based deep learning (DL) model is introduced to objectively determine the nature of an SM message. The proposed model uses the convolution neural network and bidirectional long short-term memory network layers.

The developed system leads to a better performance in accuracy, precision, recall, F-score, area under receiver operating characteristic curve and area under precision-recall curve, compared to other state-of-the-art methods in the literature. The contribution of this paper is three fold. First, it presents a new covid data set of SM messages with the label of nature of the message. Second, it offers a fusion-based DL model to classify SM data. Third, it presents a Web-based interface to visualize the structured information.

The architecture of DisDSS is analyzed based on the practical case study, i.e. COVID-19. The proposed DL-based model is embedded into a Web-based interface for decision support. To the best of the authors’ knowledge, this is India’s first SM-based DM system.

This study aims to propose a novel deep learning (DL)-based framework, iRelevancy, for identifying the disaster relevancy of a social media (SM) message.

It is worth mentioning that a fusion-based DL model is introduced to objectively identify the relevancy of a SM message to the disaster. The proposed system is evaluated with cyclone Fani data and compared with state-of-the-art DL models and the recent relevant studies. The performance of the experiments is assessed by the accuracy, precision, recall, f1-score, area under receiver operating curve and precision–recall curve score.

The iRelevancy leads to a better performance in accuracy, precision, recall, F-score, the area under receiver operating characteristic and area under precision-recall curve, compared to other state-of-the-art methods in the literature.

The predictive performance of the proposed model is illustrated with experimental results on cyclone Fani data, along with misclassifications. Further, to analyze the performance of the iRelevancy, the results on other cyclonic disasters, i.e. cyclone Titli, cyclone Amphan and cyclone Nisarga are presented. In addition, the framework is implemented on catastrophic events of different natures, i.e. COVID-19. The research study can assist disaster managers in effectively maneuvering disasters during distress.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.

This study aims to investigate barriers and enablers of social media usage by zooming on one specific type of domain: disaster management. First, by systematically reviewing previous studies using a typology to social media usage, this study identifies the challenges often faced. Second, the results are visualized by qualitatively analyzing the focus group discussion data.

This paper opted for an inductive thematic approach of grounded theory, including focus group discussion with ten participants from diverse backgrounds working in the disaster domain. The data is transcribed verbatim and coded using Atlas.ti software.

The findings suggest that the vogue of social media significantly ascends its usage in disaster management. Regulatory, software, physical, authenticity, cultural and demographic rose as challenges for social media usage in disaster management. Findings further indicate enablers as the rise in mobile penetration, democratic participation, increase in living standards, two-way real-time communication, global reach, expeditious decision-making, no space-time constraint and cheaper source of information. Social media, compared to traditional media, is explored. This study has practical implications in helping authorities understand the barriers and enablers for social media usage in disaster management.

Qualitative data analysis of social media usage for disaster management has received scant attention. The main takeaway of this research is to offer clear findings of the purview of social media usage for disaster management. It demonstrates the challenges and enablers of disaster management using social media in the Indian context. Results indicate that leveraging social media for disaster management can extend decision-making for effective disaster management.