Identification of Influencer with Machine Learning Using Centrality Measures in Social Networks

Year 2024, , 166 - 172, 30.04.2024

Aybike Şimşek

https://doi.org/10.53433/yyufbed.1348472

Abstract

Detection of influencers in social networks is an essential problem for many areas in practice, such as public opinion shaping, viral marketing, and preventing the spread of rumors. For this, it is necessary to estimate how much influence each individual has according to their position on the network. For this purpose, network centrality measures, which provide information about the position of individuals on the network, are frequently used in the literature. Most existing studies try to rank individuals on social networks according to their influence and thus identify the most influential people. On the other hand, a tiny percentage of individuals on a social network are influencers. In this regard, instead of trying to rank all individuals according to their influence, it is sufficient for many applications to divide potential influencers and other individuals into two classes. In this study, we considered the detection problem of influencers as a binary classification problem. We determined the centrality measures of individuals as features and classified the individuals as the influencers and the others with the Decision Tree classifier. Experimental studies have shown that the Decision Tree classifier gives more successful results than the basic centrality measures.

Keywords

Centraliy Measures, Influencer, Machine Learning, Social networks

Sosyal Ağlarda Merkezilik Ölçütleri Kullanılarak Makine Öğrenmesi İle Etkili Bireylerin Tespiti

Abstract

Sosyal ağlardaki etkili bireylerin tespiti, kamuoyu şekillendirme, viral pazarlama, dedikodu yayılımını önleme gibi pratikte birçok alan için önemli bir problemdir. Bunun için her bir bireyin ne kadar etkiye sahip olduğunun, bireyin ağ üzerindeki konumuna göre tahmin edilmesi gerekmektedir. Bu amaçla, bireylerin ağ üzerindeki konumları ile ilgili bilgi veren ağ merkezilik ölçütleri literatürde sıklıkla kullanılmaktadır. Mevcut çalışmaların büyük bir kısmı, sosyal ağlardaki bireyleri etkilerine göre sıralamaya ve bu şekilde en etkili kişileri tespit etmeye çalışırlar. Öte yandan, bir sosyal ağ üzerindeki bireylerin çok küçük bir kısmı gerçekten etkili bireydir. Bu bakımdan, bütün bireyleri etkilerine göre bir sıraya koymaya çalışmak yerine, etkili olabilecek bireyleri ve diğer bireyleri iki sınıfa ayırmak birçok uygulama için yeterlidir. Biz bu çalışmada, etkili birey tespiti problemini ikili sınıflandırma problemi olarak ele aldık. Bireylerin merkeziyet ölçütlerini birer öznitelik olarak belirleyip, Karar Ağacı sınıflandırıcı ile bireyleri etkili ve değil şeklinde sınıflandırdık. Deneysel çalışmalar; Karar Ağacı sınıflandırıcının, temel merkezilik ölçütlerine göre daha başarılı sonuçlar verdiğini göstermiştir.

Keywords

Etkili Birey, Makine Öğrenmesi, Merkezilik ölçütleri, Sosyal ağlar

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