气候变化领域集成服务门户(CCPortal): Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter

CCPortal

DOI	10.1007/s11069-021-04527-w
	Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter
	Dereli T.; Eligüzel N.; Çetinkaya C.
发表日期	2021
ISSN	0921030X
起始页码	2025
结束页码	2045
卷号	106 期号:3
英文摘要	Intensity of natural disasters has substantially increased; disaster management has gained importance along with this reason. In addition, social media has become an integral part of disaster management. Before, during and after disasters; people use social media and large number of output is obtained through social media activities. In this regard, Twitter is the most popular social media tool as micro blogging. Twitter has also become significant in complex disaster environment for coordinating events. It provides a swift way to collect crowd-sourced information. So, how do humanitarian organizations use Twitter platform? Humanitarian organizations utilize resources and related information while managing disasters. The effective use of social media by humanitarian agencies causes increased peoples’ awareness. The international federation of red cross and Red Crescent Societies (IFRC) is the most significant humanitarian organization that aims providing assistance to people. Thus, the aim of this paper is to analyze IFRC’s activities on Twitter and propose a perspective in the light of theoretical framework. Approximately, 5201 tweets are passed the pre-processing level, some important topics are extracted utilizing word labeling, latent dirichlet allocation (LDA model) and bag of Ngram model and sentiment analysis is applied based on machine learning classification algorithms including Naïve Bayes, support vector machine SVM), decision tree, random forest, neural network and k-nearest neighbor (kNN) classifications. According to the classification accuracies, results demonstrate the superiority of support vector machine among other classification algorithms. This study shows us how IFRC uses Twitter and which topics IFRC emphasizes more. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
关键词	Content analyses IFRC LDA Machine learning Topic labeling Twitter
英文关键词	algorithm; disaster management; machine learning; natural disaster; nonprofit organization; social media; support vector machine; theoretical study
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206184
作者单位	Office of the President, Hasan Kalyoncu University, Gaziantep, Turkey; Industrial Engineering, Gaziantep University, Gaziantep, 27310, Turkey; Department of Management Information Systems, Adana Alparslan Turkes Science and Technology University, Adana, 01250, Turkey
推荐引用方式 GB/T 7714	Dereli T.,Eligüzel N.,Çetinkaya C.. Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter[J],2021,106(3).
APA	Dereli T.,Eligüzel N.,&Çetinkaya C..(2021).Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter.Natural Hazards,106(3).
MLA	Dereli T.,et al."Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter".Natural Hazards 106.3(2021).