The Role of Artificial Intelligence in Social Media Big data Analytics for Disaster Management -Initial Results of a Systematic Literature Review

Abstract

When any kind of disaster occurs, victims who are directly and indirectly affected by the disaster often post vast amount of data (e.g., images, text, speech, video) using numerous social media platforms. This is because social media has recently become a primary communication channel among people to report either to public or to emergency responders (ERs). ERs, who are from various emergency response organizations (EROs), usually consider to gain awareness of the situation in order to respond to occurred disaster. However, with the occurrence of the disaster, within minutes, the social media platforms are flooded with various kinds of data which become overwhelmed for ERs with big data. Further, in this posted data, there may be majority of the data consist of redundant and irrelevant content. With this, it becomes challenging for ERs to make sense and take decisions of/on the available big data. Despite recent advances in the technology, processing and analyzing of the disaster related social media big data remains a challenging task. Hence, in this paper, we focus on presenting an initial analysis of a systematic literature review on application of artificial intelligence to analyze/process social media big data for efficient disaster management. During a systematic review process 68 publications were identified. Thereafter, we analyzed all the identified papers. From our analysis, we conclude that the most of the reviewed papers are on text and image classification and mostly convolutional neural networks have been employed for the classification.