TY  - JOUR
T1  - Detecting Fake News on Big Data
TT  - Büyük Veri Üzerindeki Sahte Haberlerin Tespit Edilmesi
AU  - Dökeroğlu, Tansel
AU  - Subaşı, Begüm
AU  - Beral, Hilalnur
AU  - Güleç, Nilüfer
PY  - 2021
DA  - December
JF  - Researcher
JO  - Researcher
PB  - Ankara Bilim Üniversitesi
WT  - DergiPark
SN  - 2717-9494
SP  - 1
EP  - 5
VL  - 01
IS  - 02
LA  - en
AB  - In this study, we developed a new framework for detecting fake news, which has recently become a significant problem in social media. We compared the performances of different machine learning approaches. It becomes a challenging problem to detect fake news effectively. Apache Spark’s machine learning environment, where many processors can work simultaneously, offers a very suitable environment for dealing with big data classification problems. After experiments using Naïve Bayes, Neural Network, Logistic regression, and Support Vector Machine on large datasets we obtained on Kaggle showed that our software can report up to 99% accuracy rates.
KW  - Fake news
KW  - Machine learning
KW  - Big data
KW  - classification
N2  - Bu çalışmada, son zamanlarda sosyal medyada önemli bir sorun haline gelen yalan haberlerin tespiti için yeni bir çerçeve geliştirilmiştir. Farklı makine öğrenimi yaklaşımlarının performanslarını karşılaştırılmıştır. Sahte haberleri etkili bir şekilde tespit etmek zorlu bir sorun haline gelmektedir. Birçok işlemcinin aynı anda çalışabildiği Apache Spark makine öğrenme ortamı, büyük veri sınıflandırma problemlerinin üstesinden gelmek için oldukça uygun bir ortam sunmaktadır. Naive Bayes, Yapay Sinir Ağı, Lojistik regresyon ve Destek Vektör Makinesi kullanılarak Kaggle'da elde ettiğimiz büyük veri kümeleri üzerinde yapılan deneylerden sonra, yazılımımızın %99'a varan doğruluk oranları rapor edebildiğini göstermiştir.
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UR  - https://dergipark.org.tr/tr/pub/researcher/issue//984460
L1  - https://dergipark.org.tr/tr/download/article-file/1931902
ER  -