Abstract

This study investigates the collection of social media signals without

exclusive reliance on application programming interfaces (APIs). The current

research on collecting social signals for disaster management is primarily focused

on APIs. This approach is valuable, but it also presents a range of challenges that

need to be taken into account. Thus, the extent and regularity of data collection

may be impacted, posing challenges in obtaining comprehensive and up-to-date

information. In light of this knowledge gap, we put forth a compelling argument

in support of non-API approaches for gathering social signals from social media

platforms. To answer the research questions, a qualitative methodology

employing an inductive approach was used to gather and analyze data from

officers working in disaster management organizations (DMOs). By adopting

this approach, noteworthy themes and patterns emerged and were carefully

examined, ultimately resulting in the derivation of the research findings. The

study highlights the potentials of social signals in enhancing decision-making

across various phases of disaster management. Through innovative techniques,

DMOs can leverage social signals from public posts, comments, and interactions

to gain insights into user sentiments, opinions, and real-time updates. These

insights greatly assist decision-making at different stages of disaster

management, including preparedness, response, recovery, and mitigation.

Overall, the study emphasizes the effectiveness of gathering social signals from

social media platforms without relying solely on APIs, highlighting their

potential to improve decision-making in disaster management.

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