Abstract
Twitter, a microblogging site plays a vital role in spreading information during natural disasters. The volume of tweets posted during crisis and disaster tend to be extremely high, making it hard for disaster-affected communities and disaster management team of a local government unit to process the information in a timely manner. In this research, we describe different data mining techniques that can be used for extracting information from microblog posts that will be a basis of creating a machine learning called Disastweet: An Open-Source Tweet Mining Tool for Disaster Management. Specifically, we focus on extracting valuable information from tweets that is brief, self-contained relevant to disaster response.
Conflict of Interest
The authors declare no conflict of interest.
Ethical Approval
Not applicable
Data Availability
The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].
Funding
This work did not receive any external funding.
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