Advertisement

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)

Abstract

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.

Keywords

Satire identification Fake news Machine learning 

References

  1. 1.
    Ramsey, R.: Affect and political satire: how political TV satire implicates internal political efficacy and political participation. University of the Pacific, MA Thesis (2018)Google Scholar
  2. 2.
    Fersini, E., Messina, E., Pozzi, F.A.: Sentiment analysis: Bayesian ensemble learning. Decis. Support Syst. 68, 26–38 (2014)CrossRefGoogle Scholar
  3. 3.
    Onan, A.: Topic-enriched word embeddings for sarcasm identification. In: Silhavy, R. (ed.) CSOC 2019. AISC, vol. 984, pp. 293–304. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-19807-7_29CrossRefGoogle Scholar
  4. 4.
    Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)CrossRefGoogle Scholar
  5. 5.
    Poria, S., Cambria, E., Hazarika, D., Vij, P.: A deeper look into sarcastic tweets using deep convolutional neural networks. In: Proceedings of COLING 2016, pp. 1601–1612. ACM, New York (2016)Google Scholar
  6. 6.
    Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 107–116. ACM, New York (2010)Google Scholar
  7. 7.
    Gonzalez-Ibanez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in Twitter: a closer look. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 581–586. ACM, New York (2011)Google Scholar
  8. 8.
    Filatova, E.: Irony and sarcasm: corpus generation and analysis using crowdsourcing. In: Proceedings of Language Resources and Evaluation Conference, pp. 392–398. ACM, New York (2012)Google Scholar
  9. 9.
    Salas-Zarate, M., Paredes-Valverde, M.A., Rodriguez-Garcia, M.A., Valencia-Garica, R., Alor-Hernandez, G.: Automatic detection of satire in Twitter: a psycholinguistic-based approach. Knowl.-Based Syst. 128, 20–33 (2017)CrossRefGoogle Scholar
  10. 10.
    Ahmad, T., Akhtar, H., Chopra, A., Akhtar, M.W.: Satire detection from web documents using machine learning methods. In: Proceedings of International Conference on Soft Computing and Machine Intelligence, pp. 102–105. IEEE, New York (2014)Google Scholar
  11. 11.
    Barbieri, F., Ronzano, F., Saggion, H.: Is this tweet satirical? a computational approach for satire detection in Spanish. Procesamiento del Lenguaje Nat. 55, 135–142 (2015)Google Scholar
  12. 12.
    Barbieri, F., Ronzano, F., Saggion, H.: Do we criticise (and laugh) in the same way? automatic detection of multi-lingual satirical news in Twitter. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 1215–1221. AAAI Press, New York (2015)Google Scholar
  13. 13.
    Rubin, V., Conroy, N., Chen, Y., Cornwell, S.: Fake news or truth? using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pp. 7–17. ACL, New York (2016)Google Scholar
  14. 14.
    Delmonte, R., Stingo, M.: Detecting satire in italian political commentaries. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9876, pp. 68–77. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45246-3_7CrossRefGoogle Scholar
  15. 15.
    Perez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)
  16. 16.
    Ahmed, H., Traore, I., Saad, S.: Detection of online fake news using n-gram analysis and machine learning techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) ISDDC 2017. LNCS, vol. 10618, pp. 127–138. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69155-8_9CrossRefGoogle Scholar
  17. 17.
    Yang, F., Mukherjee, A., Dragut, E.: Satirical news detection and analysis using attention mechanism and linguistic features. arXiv preprint arXiv:1709.01189 (2017)
  18. 18.
    Ravi, K., Ravi, V.: Irony detection using neural network language model, psycholinguistic features and text mining. In: Proceedings of IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, pp. 254–260. IEEE, New York (2018)Google Scholar
  19. 19.
    Onan, A.: Classifier and feature set ensembles for web page classification. J. Inf. Sci. 42(2), 150–165 (2016)CrossRefGoogle Scholar
  20. 20.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  21. 21.
    Kantardzic, M.: Data Mining: Concepts, Models, Methods and Algorithms. Wiley, Hoboken (2011)zbMATHCrossRefGoogle Scholar
  22. 22.
    Gehrke, J.: The Handbook of Data Mining. Lawrence Erlbaum Associates, Chicago (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

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

Personalised recommendations