A deep multi-modal neural network for informative Twitter content classification during emergencies

YesPeople start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents

The emergent role of digital technologies in the context of humanitarian supply chains: a systematic literature review

The role of digital technologies (DTs) in humanitarian supply chains (HSC) has become an increasingly researched topic in the operations literature. While numerous publications have dealt with this convergence, most studies have focused on examining the implementation of individual DTs within the HSC context, leaving relevant literature, to date, dispersed and fragmented. This study, through a systematic literature review of 110 articles on HSC published between 2015 and 2020, provides a unified overview of the current state-of-the-art DTs adopted in HSC operations. The literature review findings substantiate the growing significance of DTs within HSC, identifying their main objectives and application domains, as well as their deployment with respect to the different HSC phases (i.e., Mitigation, Preparedness, Response, and Recovery). Furthermore, the findings also offer insight into how participant organizations might configure a technological portfolio aimed at overcoming operational difficulties in HSC endeavours. This work is novel as it differs from the existing traditional perspective on the role of individual technologies on HSC research by reviewing multiple DTs within the HSC domain