Sosyal Medya Kullanımı Sırasında Adölesanların Uygunsuz İçerikle Karşılaşmasının Sınıflandırma Algoritmaları ile Tahmini: Bir Veri Madenciliği Yaklaşımı

Öz

Amaç: Bu çalışma, veri madenciliği kullanarak ergenlerin sosyal medyada uygunsuz içeriğe maruz kalmasını tahmin etmeyi amaçlamıştır.

Yöntem: Bu tanımlayıcı çalışma, 231 veri kaydı kullanılarak yürütülmüştür. Veriler, kamuya açık olan “Kaggle veri tabanı”ndan elde edilmiştir. Sosyal medya kullanımına ilişkin bilgileri içeren bir veri seti kullanılmıştır. Veri seti, sosyal medya kullanımına ilişkin değişkenleri içermektedir. Veri setine birden fazla sınıflandırma algoritması (En Yakın Komşu, Naive Bayes, Lojistik Regresyon, Destek Vektör Makineleri, Karar Ağaçları) uygulanmıştır. Bu modeller, sosyal medya kullanım değişkenlerinin bireylerin uygunsuz içeriğe maruz kalma olasılığı üzerindeki etkilerini tahmin etmek amacıyla karşılaştırmalı olarak uygulanmıştır. Performansları istatistiksel ölçütler kullanılarak değerlendirilmiştir. Bu sayede, en yüksek doğruluk ve güvenilirlik sağlayan yöntemlerin belirlenmesi hedeflenmiştir.

Bulgular: Her yöntemin uygunsuz içeriği tahmin etmedeki etkinliği değerlendirilmiş ve en iyi performans gösteren model belirlenmiştir. Karar ağaçlarının, En Yakın Komşu yönteminden sonra tahmin modelinde en iyi performansı gösterdiği belirlenmiştir. Naive Bayes, Lojistik Regresyon ve Destek Vektör Makineleri modellerinin performanslarının eşit olduğu ve diğer modellere kıyasla performanslarının daha düşük olduğu belirlenmiştir.

Sonuç: Oluşturulan tahmin modelinin sonuçlarına göre; 17-19 yaş aralığında bulunan adölesan bireylerin kullandıkları sosyal medya platformlarının sayısında ve bu platformlarda günlük olarak geçirdikleri süre miktarında meydana gelen artışın, doğrudan bir şekilde uygunsuz içeriklerle karşılaşma sıklığını da artıracağı öngörülmektedir. Model, sosyal medya kullanım düzeyindeki artışın, özellikle denetimsiz ya da filtrelenmemiş içeriklerle karşılaşma olasılığını yükselttiğini ortaya koymaktadır.

Anahtar Kelimeler

• Adölesan
• Halk Sağlığı
• Hemşire
• Sınıflandırma
• Sosyal Medya Bağımlılığı
• Veri Madenciliği.

Predicting Adolescents' Exposure to Inappropriate Content during Social Media Use Using Classification Algorithms: A Data Mining Approach

Öz

Aim: This study aimed to predict adolescents’ exposure to inappropriate social media content using data mining.

Methods: This descriptive study was conducted using 231 data records. The data was obtained from the publicly available “Kaggle database”. A dataset containing information related to social media usage was used. The dataset included variables related to social media use. Multiple classification algorithms (Nearest Neighbor, Naive Bayes, Logistic Regression, Support Vector Machines, Decision Trees) were applied to the dataset. These models were applied comparatively to predict the effects of social media usage variables on individuals’ likelihood of exposure to inappropriate content. Their performance was evaluated using statistical metrics. This aimed to identify the methods providing the highest accuracy and reliability.

Results: The effectiveness of each method in predicting inappropriate content was evaluated and the best performing model was determined. It was determined that decision trees showed the best performance in the prediction model after Nearest Neighbor. It was determined that the performances of Naive Bayes, Logistic Regression and Support Vector Machines models were equal and their performances were lower compared to other models.

Conclusion: According to the results of the prediction model, it is predicted that the increase in the number of social media platforms used by adolescents in the 17-19 age range and the amount of time they spend on these platforms on a daily basis will directly increase the frequency of encountering inappropriate content. The model reveals that the increase in the level of social media use increases the likelihood of encountering unsupervised or unfiltered content.

Anahtar Kelimeler

• Adolescent
• Public Health
• Nurse
• Classification
• Social Media Addiction
• Data Mining.

Kaynakça

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