Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter

Türkay Dereli, Nazmiye Eligüzel () and Cihan Çetinkaya
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Türkay Dereli: Hasan Kalyoncu University
Nazmiye Eligüzel: Gaziantep University
Cihan Çetinkaya: Adana Alparslan Turkes Science and Technology University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 106, issue 3, No 14, 2025-2045

Abstract: Abstract 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.

Keywords: Content analyses; IFRC; Topic labeling; LDA; Machine learning; Twitter (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04527-w

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