A topic model based framework for identifying the distribution of demand for relief supplies using social media data

ABSTRACT

Natural disasters have caused substantial economic losses and numerous casualties. The demand analysis of relief supplies is the premise and basis for efficient relief operations after disasters. With the widespread use of social media, it has become a vital channel for people to report their demand for relief supplies and provides a way to obtain information on disaster areas. Therefore, we present a topic model-based framework and establish a demand dictionary and a gazetteer that aims to identify the spatial distribution of the demand for relief supplies by using social media data. Taking the 2013 Typhoon Haiyan (also called Yolanda) as a case study, we identify the potential topics of tweets with the biterm topic model, screen the tweets related to demands, and obtain the demand and location information from tweets to study the distribution of the relief supplies needs. The results show that, based on the demand dictionary, a gazetteer and the biterm topic model, the effective demand for relief supplies can be extracted from tweets. The proposed framework is feasible for the identification of accurate demand information and its distribution. Further, this framework can be applied to other types of disaster responses and can facilitate relief operations.

KEYWORDS:

• Demand for relief supplies
• social media
• btm
• nature disaster
• twitter
• emergency management

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in [figshare.com] with the identifier(s) [https://doi.org/10.6084/m9.figshare.11352590.v3]. Complete Twitter data for Typhoon Haiyan cannot be made publicly available to protect research participant privacy and consent, but sample data are provided for experiments.

Supplementary material

Supplemental data for this article can be accessed here.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Key Research and Development Plan of China [Grant No. 2017YFB0504102]; Fundamental Research Funds for the Central Universities [Grant No. 2019NTST01]; National Natural Science Foundation of China [Grant No. 41771537].

Notes on contributors

Ting Zhang

Ting Zhang is a master student at the Center for Geodata and Analysis, Beijing Normal University, majoring in cartography and geographic information systems. Her fields of interest are natural disasters, social media data analysis and spatio-temporal statistical analysis.

Shi Shen

Shi Shen, PhD, lecturer, works at the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University. His fields of interest are spatio-temporal statistical analysis, geographical information science,  geography complexity, and human-earth coupled model.

Changxiu Cheng

Changxiu Cheng is a Professor at the Center for Geodata and Analysis, Beijing Normal University, 100875, Beijing, China. E-mail: [email protected]. Her research interest focus on the study of spatial and temporal differentiations of natural disasters and land use change, and the analysis of big geographical data etc.

Kai Su

Kai Su is a master of Beijing Normal University, majoring in cartography and geographic information systems. His fields of interest are natural disasters and public data analysis.

Xiangxue Zhang

Xiangxue Zhang is a PhD student, majors in cartography and geographic information systems at the Center for Geodata and Analysis, Beijing Normal university. Her fields of interest are air pollution, natural disaster, public heailth and spatio-temporal statistical analysis.