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A state-of-art optimization method for analyzing the tweets of earthquake-prone region

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Abstract

With the increase in accumulated data and usage of the Internet, social media such as Twitter has become a fundamental tool to access all kinds of information. Therefore, it can be expressed that processing, preparing data, and eliminating unnecessary information on Twitter gains its importance rapidly. In particular, it is very important to analyze the information and make it available in emergencies such as disasters. In the proposed study, an earthquake with the magnitude of Mw = 6.8 on the Richter scale that occurred on January 24, 2020, in Elazig province, Turkey, is analyzed in detail. Tweets under twelve hashtags are clustered separately by utilizing the Social Spider Optimization (SSO) algorithm with some modifications. The sum-of intra-cluster distances (SICD) is utilized to measure the performance of the proposed clustering algorithm. In addition, SICD, which works in a way of assigning a new solution to its nearest node, is used as an integer programming model to be solved with the GUROBI package program on the test data-sets. Optimal results are gathered and compared with the proposed SSO results. In the study, center tweets with optimal results are found by utilizing modified SSO. Moreover, results of the proposed SSO algorithm are compared with the K-means clustering technique which is the most popular clustering technique. The proposed SSO algorithm gives better results. Hereby, the general situation of society after an earthquake is deduced to provide moral and material supports.

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Notes

  1. Statista, https://www.statista.com/statistics/263105/development-of-the-number-of-earthquakes-worldwide-since-2000/.

  2. AFAD, https://www.afad.gov.tr/elazig-depremi-sonrasi-yapilan-yardimlar-merkezicerik.

  3. Statista, https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/.

  4. Statista, https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users//.

  5. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.php.

  6. MathWorks, http://www.mathworks.com/help/textanalytics/ref/fitlsa.html

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Authors and Affiliations

  1. Industrial Engineering, Gaziantep University, 27310, Gaziantep, Turkey

    Nazmiye Eligüzel

  2. Department of Management Information Systems, Adana Alparslan Türkeş Science and Technology University, 01250, Adana, Turkey

    Cihan Çetinkaya

  3. Office of the President, Hasan Kalyoncu University, Gaziantep, Turkey

    Türkay Dereli

Authors
  1. Nazmiye Eligüzel
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  2. Cihan Çetinkaya
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  3. Türkay Dereli
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Correspondence to Nazmiye Eligüzel.

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Eligüzel, N., Çetinkaya, C. & Dereli, T. A state-of-art optimization method for analyzing the tweets of earthquake-prone region. Neural Comput & Applic 33, 14687–14705 (2021). https://doi.org/10.1007/s00521-021-06109-0

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