KNOWLEDGE MANAGEMENT OF SOCIAL MEDIA DATA FOR DISASTER MANAGEMENT

Abstract

Disasters affect the lives, and infrastructure in a negative manner. With the internet fad, social media has become inevitable in our lives, generating tsunami of data. An individual cannot retrieve the same message on social media within a blink of eye. During disastrous times, data is even more critical. People use social media at catastrophic times. Data, being raw, and unstructured, needs to be in a knowledgable format, so that effective decisions can be taken at right time. The overarching aim of this dissertation is to advance the management of knowledge for disaster management using social media data. Conventional knowledge management systems are not optimal enough to support disaster management processes. The dynamic nature of disasters offer different situations. With the evolving times, the knowledge management systems needs to be evolved to handle complex environments. In the first objective of our research work, we explore the challenges and enablers of social media usage for disaster management by understanding views and perspectives of people working in disaster management domain. This was done with the help of literature review and data collected through focus group discussion. The participants chosen for focus group discussion are homogeneously working in disaster management domain but are from heterogeneous backgrounds like civil, architecture, mechanical, management and computer science. The number of participants are 10, ranging from 21 to 42 years of age. 8 male participants and 2 female participants are there. Half of the participants are Master students pursuing disaster management. The remaining four participants are doctoral students and one professor of Centre of Excellence in Disaster Management is amongst the 10 participants. The methodology developed is in concoction of existing literature and the efficacy of qualitative data obtained from focus group discussion, using Atlas.ti software following inductive thematic approach. The transcripts are transcribed manually by the moderator. After acquiring validation from the participants, raw data is categorized using inductive thematic approach in Atlas.ti software. The results are finalized after expert validation. The themes are developed using the panoply of coding functions - Open coding, Quick coding, List coding and In-vivo coding - available in Atlas.ti 8. The identified challenges are physical, software, cultural, demographic, authenticity, and regulatory.