Sahte Haber Tespiti için Kullanılan Modellerin Sosyal Bağlam Kapsamında İncelenmesi

Geleneksel haber medyasında, sahte haber tespiti için haberin içeriği esas alınırken, sosyal medyada sosyal bağlam bilgileri sahte haberleri tespit etmeye yardımcı olmak için kullanılabilmektedir. Sosyal bağlam, verilerin sosyal medyada dağıtımı ve çevrimiçi kullanıcıların birbirleri ile etkileşimi de göz önünde bulundurularak haberlerin sosyal çevrede yayılımını da açıklayarak haberlerin doğru olup olmadığını tespit etmek maksadıyla gerekli bilgileri sağlamaktadır. Sosyal medya, haber içeriğine dayalı modelleri desteklemektedir. Bu modelleri geliştirmek araştırmacılar için ek bazı kaynaklar sunmaktadır. Sosyal bağlam bilgisi kullanıcı detayı, gönderi ve ağ analizi olmak üzere üç ana başlığı temsil etmektedir. Bu çalışmada veri bilimi perspektifinden sahte haberlerin sosyal bağlama dayalı özellikleri ve modelleri konusunda derleme çalışması yapılmıştır. Literatürde bu özellik ve modelleri kullanan çalışmalar hem makine öğrenmesi hem de derin öğrenme yaklaşımıyla incelenmiştir. Öznitelik çıkarımı ve sahte haber tespitine yönelik oluşturulan 9 adet bilinen veri setinin analizi yapılmıştır.

Anahtar Kelimeler:

yanlış bilgilendirme, uydurma haber, sahte haber tespiti, sosyal bağlam

Examining the Models Used for Fake News Detection in the Scope of Social Context

While in traditional news media, the content of the news is based on fake news detection, social context information in social media can be used to help detect fake news. Considering the social context, the distribution of data on social media and the interaction of online users with each other, it also explains the dissemination of news in the social environment and provides the necessary information to determine whether the news is true or not. Social media supports models based on news content. Developing these models provides some additional resources for researchers. Social context information represents three main topics: user detail, post and network analysis. In this study, a compilation study was conducted on the social context-based features and models of fake news from a data science perspective. Studies using these features and models in the literature have been examined with both machine learning and deep learning approaches. Analysis of 9 known data sets created for feature extraction and fake news detection was performed.

Keywords:

misinformation, fabricated news, fake news detection, social context,

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