Finding Influencers on Twitter with Using Machine Learning Classification Algorithms

Abstract

Microblog sites are environments where people follow people. With this feature, a microblog site is a convenient environment for spreading an opinion or introducing a new product. The key point is determination of individuals who maximize the spreading. This problem is known as Influence Maximization (IM) and has attracted attention of many researchers. Many studies in the literature have modeled IM problem on graphs for different propagation models such as Independent Cascade (IC) and Linear Threshold (LT). However, microblogs like Twitter have their own features. Many works on IM in Twitter derive new metrics from user and tweet features; apply a greedy approach for selecting influencers. In this study, we adopted different approach for IM problem, and we dealt it as a classification problem. Firstly, we collected data on International Women Day 2018; empirically we labeled the users as either influencer candidates or non-influencers; then we applied classification methods for classifying users into one class with using features of users. By this way, we obtained an influencer candidates set, which is very smaller than entire dataset. Experimental results show that making selection with using same heuristic (namely MF) from the reduced influencer candidates set outperforms making selection from entire dataset.

Keywords

• Influence Maximization,Twitter,Social Networks,Microblog,Classification

Twitter Üzerindeki Etkili Bireylerin Makine Öğrenmesi Sınıflandırma Algoritmaları İle Tespiti

Abstract

Mikroblog siteleri insanların birbirlerini takip ettikleri ortamlardır. Bu özellikleri ile bir microblog sitesi bir fikrin ya da yeni bir ürünün yayılması için elverişli bir ortamdır. Buradaki anahtar nokta, yayılımı maksimize edecek bireylerin tespitidir. Bu problem, Etki Maksimizasyonu (EM) olarak bilinir ve birçok araştırmacının ilgisini çekmiştir. Literatürdeki birçok çalışma EM problemini graflar üzerinde Independent Cascade (IC) ve Linear Threshold (LT) yayılım modelleri için ele almıştır. Ne var ki, Twitter gibi microblog sitelerinin kendi özellikleri ve vardır. Twitter üzerinde EM problemini ele almış olan birçok çalışma, kullanıcı ve tweet özelliklerinden yeni ölçütler geliştirme ve bu ölçütleri kullanan bir aç gözlü algoritma ile etkin bireyleri seçme yolunu izler. Bu çalışmada biz EM problemi farklı bir yaklaşım uyguladık ve problemi bir sınıflandırma problemi olarak ele aldık. İlk olarak, 2018 Uluslararası Kadınlar Gününde veri topladık; kullanıcıları deneysel olarak etkili bireyler ve etkili olmayan bireyler olarak etiketledik; son olarak bireyleri etkili ya da etkili olmayan diye sınıflara ayırmak için sınıflandırma algoritmalarını kullandık. Bu şekilde, ana verisetinden oldukça küçük olan bir etkili bireyler kümesi elde ettik. Deneysel sonuçlar, aynı parametreyi kullanarak indirgenmiş kümeden seçim yapılmasının, bütün veriseti üzerinden seçim yapılmasına göre çok daha başarılı sonuçlar verdiğini göstermiştir.

Keywords

• Etki Maksimizasyonu,Twitter,Sosyal Ağlar,Mikroblog,Sınıflandırma

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