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.
