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

Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review.

Mechanisms for sharing information in a disaster situation have drastically changed due to new technological innovations throughout the world. The use of social media applications and collaborative technologies for information sharing have become increasingly popular. With these advancements, the amount of data collected increases daily in different modalities, such as text, audio, video, and images. However, to date, practical Disaster Response (DR) activities are mostly depended on textual information, such as situation reports and email content, and the benefit of other media is often not realised. Deep Learning (DL) algorithms have recently demonstrated promising results in extracting knowledge from multiple modalities of data, but the use of DL approaches for DR tasks has thus far mostly been pursued in an academic context. This paper conducts a systematic review of 83 articles to identify the successes, current and future challenges, and opportunities in using DL for DR tasks. Our analysis is centred around the components of learning, a set of aspects that govern the application of Machine learning (ML) for a given problem domain. A flowchart and guidance for future research are developed as an outcome of the analysis to ensure the benefits of DL for DR activities are utilized.Publishe