Veri Madenciliği Uygulamalarının Web Tabanlı Mekânsal Görsel Analitik Ortamda Sunumu: COVID-19 Aşı Tweet’leri Örneği

Yıl 2023, , 417 - 426, 03.05.2023

Burak Çağlar , Hüseyin Zahit Selvi

https://doi.org/10.35414/akufemubid.1206851

Öz

Mekânsal görsel analitik, mekânsal bilgilerin etkileşimli görsel ara yüzlerle ele alındığı analitik akıl
yürütme bilimidir. Mekânsal görsel analitik sistemleri sayesinde, Twitter gibi sosyal medya
platformlarındaki büyük veri setlerinden bir konu hakkında elde edilen veriler son kullanıcıya etkileşimli
haritalama sistemleriyle sunulabilir. 11 Mart 2020’de Dünya Sağlık Örgütü’nün COVID-19 salgınını
duyurmasının ardından Twitter veri trafiğinde de ciddi bir artış görülmüştür. Bu çalışmada, COVID-19
salgını döneminin önemli tartışmalarından biri olan COVID-19 aşıları hakkındaki tweet trafiğinin
zamansal ve mekânsal gelişimi veri madenciliği teknikleriyle incelenmiş ve görsel analitik ortamda
sunulmuştur. Bu çalışma ile twitter gibi sosyal medya platformlarının sahip olduğu büyük veri olarak
kabul edilen veri setlerinin veri madenciliği yöntemleriyle analiz edilerek afet ve kriz yönetimi açısından
önemli çıkarımlar yapılabileceği ortaya konmuştur.

Anahtar Kelimeler

Mekânsal görsel analitik, Veri Madenciliği, Büyük Veri, Etkileşimli Haritalama, Sosyal Medya

Presentation of Data Mining Applications in Web Based Geovisual Analytical Environment: Example of COVID-19 Vaccine Tweets

Öz

Spatial visual analytics is the science of analytical reasoning in which spatial information is handled with
interactive visual interfaces. Thanks to spatial visual analytics systems, data obtained from large data
sets on social media platforms such as Twitter can be presented to the end user with interactive
mapping systems. After the World Health Organization announced the COVID-19 outbreak on March
11, 2020, there has been a significant increase in Twitter data traffic. In this study, the temporal and
spatial development of tweet traffic about COVID-19 vaccines, which is one of the important discussions
of the COVID-19 epidemic period, was examined with data mining techniques and presented in a visual
analytical environment. With this study, it has been revealed that important inferences can be made in
terms of disaster and crisis management by analyzing the data sets, which are accepted as big data, of
social media platforms such as twitter with data mining methods.

Anahtar Kelimeler

Geovisual analytic, Data Mining, Big Data, Interactive Mapping, Social Media

Kaynakça

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