Using social media for classifying actionable insights in disaster scenario

Ghosh, Samujjwal and Srijith, P K and Desarkar, Maunendra Sankar (2017) Using social media for classifying actionable insights in disaster scenario. International Journal of Advances in Engineering Sciences and Applied Mathematics, 9 (4). pp. 224-237. ISSN 0975-0770

Full text not available from this repository. (Request a copy)


Micro-blogging sites are important source of real-time situational information during disasters such as earthquakes, hurricanes, wildfires, flood etc. Such disasters cause miseries in the lives of affected people. Timely identification of steps needed to help the affected people in such situations can mitigate those miseries to a large extent. In this paper, we focus on the problem of automated classification of disaster related tweets to a set of predefined categories. Some example categories considered are resource availability, resource requirement, infrastructure damage etc. Proper annotation of the tweets with these class information can help in timely determination of the steps needed to be taken to address the concerns of the people in the affected areas. Depending on the information category, different feature sets might be useful for proper identification of posts belonging to that category. In this work, we define multiple feature sets and use them with various supervised classification algorithms from literature to study the effectiveness of our approach in annotating the tweets with their appropriate information categories.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Disaster management, Information retrieval, Social media, Text categorization
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 18 Jan 2018 04:49
Last Modified: 18 Jan 2018 04:49
Publisher URL:
OA policy:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 3732 Statistics for this ePrint Item