Satire Detection in Turkish News Articles: A Machine Learning Approach

  • Mansur Alp Toçoğlu
  • Aytuğ OnanEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1054)


With the advances in information and communication technologies, an immense amount of information has been shared on social media and microblogging platforms. Much of the online content contains elements of figurative language, such as, irony, sarcasm and satire. The automatic identification of figurative language can be viewed as a challenging task in natural language processing, where linguistic entities, such as, metaphor, analogy, ambiguity, irony, sarcasm, satire, and so on, have been utilized to express more complex meanings. The predictive performance of sentiment classification schemes may degrade if figurative language within the text has not been properly addressed. Satirical text is a way of figurative communication, where ideas/opinions regarding a people, event or issue is expressed in a humorous way to criticize that entity. Satirical news can be deceptive and harmful. In this paper, we present a machine learning based approach to satire detection in Turkish news articles. In the presented scheme, we utilized three kinds of features to model lexical information, namely, unigrams, bigrams and tri-grams. In addition, term-frequency, term-presence and TF-IDF based schemes have been taken into consideration. In the classification phase, Naïve Bayes, support vector machines, logistic regression and C4.5 algorithms have been examined.


Satire identification Fake news Machine learning 


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Authors and Affiliations

  1. 1.Faculty of Technology, Department of Software EngineeringManisa Celal Bayar UniversityManisaTurkey
  2. 2.Faculty of Engineering and Architecture, Department of Computer Engineeringİzmir Katip Çelebi UniversityİzmirTurkey

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