Detecting Turkish Fake News Via Text Mining to Protect Brand Integrity

Year 2022, , 323 - 333, 30.09.2022

Ozge Doguc

https://doi.org/10.54287/gujsa.1170640

Abstract

Fake news has been in our lives as part of the media for years. With the recent spread of digital news platforms, it affects not only traditional media but also online media as well. Therefore, while companies seek to increase their own brand awareness, they should also protect their brands against fake news spread on social networks and traditional media. This study discusses a solution that accurately classifies the Turkish news published online as real and fake. For this purpose, a machine learning model is trained with tagged news. Initially, the headlines were analyzed within the scope of this study that are collected from Turkish online sources. As a next step, in addition to the headlines of these news, news contexts are also used in the analysis. Analysis are done with unigrams and bigrams. The results show 95% success for the headlines and 80% for the texts for correctly classifying the fake Turkish news articles. This is the first study in the literature that introduces an ML model that can accurately identify fake news in Turkish language.

Keywords

Text Mining, Fake News, Brand Management, Data Management

References

• Ahmed, H., Traore, I., & Saad, S. (2017, October 26-28). Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: I. Traore, I. Woungang & A. Awad (Eds.), Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environment, First International Conference, ISDDC 2017, Vancouver, BC, Canada, (pp. 127–138). doi:10.1007/978-3-319-69155-8_9
• Albahar, M. (2021). A hybrid model for fake news detection: Leveraging news content and user comments in fake news. IET Information Security, 15(2), 169–177. doi:10.1049/ise2.12021
• Altunbey Özbay, F., & Alataş, B. (2020). Çevrimiçi sosyal medyada sahte haber tespiti. DÜMF Mühendislik Dergisi, 11(1), 91–103. doi:10.24012/dumf.629368
• Aytaç, Ö. B., Silahtaroğlu, G., & Doğuç, Ö. (2020). Analysis of Digital Marketing Strategies of Deposit Banks in Turkey via Text Mining Twitter Posts. In: H. Dincer & S. Yüksel (Eds.) Strategic Outlook for Innovative Work Behaviours (pp. 361–376). Springer. doi:10.1007/978-3-030-50131-0_20
• Bankole, O., & Reyneke, M. (2020). The Effect of Fake News on the Relationship between Brand Equity and Consumer Responses to Premium Brands: An Abstract. In: S. Wu, F. Pantoja & N. Krey (EdS.), Marketing Opportunities and Challenges in a Changing Global Marketplace (pp. 461–462). Springer International Publishing. doi:10.1007/978-3-030-39165-2_189
• Becker, R. (2017, June 26). Your short attention span could help fake news spread. https://www.theverge.com/2017/6/26/15875488/fake-news-viral-hoaxes-bots-information-overload-twitter-facebook-social-media
• Belin, A. (2020, June 25). How to Protect and Defend your Brand from Fake News. https://latana.com/post/fake-news-brands/
• Chen, Z. F., & Cheng, Y. (2019). Consumer response to fake news about brands on social media: the effects of self-efficacy, media trust, and persuasion knowledge on brand trust. Journal of Product & Brand Management, 29(2), 188–198. doi:10.1108/JPBM-12-2018-2145
• Chu, S. K. W., Xie, R., & Wang, Y. (2021). Cross-Language Fake News Detection. Data and Information Management, 5(1), 100–109. doi:10.2478/dim-2020-0025
• Conroy, N. K., Rubin, V. L., & Chen, Y. (2015, November 6-10). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology (ASIS&T), 52(1), 1-4. doi:10.1002/pra2.2015.145052010082
• de Souza, M. C., Nogueira, B. M., Rossi, R. G., Marcacini, R. M., dos Santos, B. N., & Rezende, S. O. (2021). A network-based positive and unlabeled learning approach for fake news detection. Machine Learning. doi:10.1007/s10994-021-06111-6
• Doguc, O., Aytac, O. B., & Silahtaroglu, G. (2020). Lemmatizer: Akıllı Türkçe kök bulma yöntemi. Turkish Studies - Information Technologies and Applied Sciences, 15(3), 289-299. doi:10.47844/TurkishStudies.44220
• Drus, Z., & Khalid, H. (2019). Sentiment Analysis in Social Media and Its Application: Systematic Literature Review. Procedia Computer Science, 161, 707-714. doi:10.1016/j.procs.2019.11.174
• Englmeier, K. (2021). The Role of Text Mining in Mitigating the Threats from Fake News and Misinformation in Times of Corona. Procedia Computer Science, 181, 149-156. doi:10.1016/j.procs.2021.01.115
• Goldberg, Y. (2017). Neural Network Methods in Natural Language Processing. Morgan & Claypool Publishers. doi:10.1007/978-3-031-02165-7
• Jiang, M., Liang, Y., Feng, X., Fan, X., Pei, Z., Xue, Y., & Guan, R. (2018). Text classification based on deep belief network and softmax regression. Neural Computing and Applications, 29(1), 61–70. doi:10.1007/s00521-016-2401-x
• Lemann, N. (2016, November 30). Solving the Problem of Fake News. https://www.newyorker.com/news/news-desk/solving-the-problem-of-fake-news
• Levin, S. (2017, June 13). Pay to sway: report reveals how easy it is to manipulate elections with fake news. https://www.theguardian.com/media/2017/jun/13/fake-news-manipulate-elections-paid-propaganda
• Mahoney, M. W. (2011). Randomized Algorithms for Matrices and Data. Foundations and Trends in Machine Learning, 3(2), 123–224. doi:10.1561/2200000035
• Mertoglu, U. (2020). A Fake News Detection Model for Turkish Language (Türkçe için Sahte Haber Tespit Modelinin Oluşturulması). PhD Thesis. Hacettepe University.
• Murphy, K. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
• Obadă, D.-R. (2019). Sharing Fake News about Brands on Social Media: a New Conceptual Model Based on Flow Theory. Argumentum. Journal of the Seminar of Discursive Logic, Argumentation Theory and Rhetoric, 17(2), 144-166.
• Parikh, S. B., & Atrey, P. K. (2018, April 10-12). Media-Rich Fake News Detection: A Survey. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), (pp. 436–441). doi:10.1109/MIPR.2018.00093
• Spicer, R. N. (2018). Lies, Damn Lies, Alternative Facts, Fake News, Propaganda, Pinocchios, Pants on Fire, Disinformation, Misinformation, Post-Truth, Data, and Statistics. In: Free Speech and False Speech, (pp 1-31). Springer International Publishing. doi:10.1007/978-3-319-69820-5_1
• Stahl, K. (2018). Fake news detection in social media. California State University Stanislaus, 6.
• Toğaçar, M., Eşidir, K. A., & Ergen, B. (2021). Yapay Zekâ Tabanlı Doğal Dil İşleme Yaklaşımını Kullanarak İnternet Ortamında Yayınlanmış Sahte Haberlerin Tespiti. Journal of Intelligent Systems: Theory and Applications, 5(1), 1–8. doi:10.38016/jista.950713
• Violos, J., Tserpes, K., Varlamis, I., & Varvarigou, T. (2018). Text Classification Using the N-Gram Graph Representation Model Over High Frequency Data Streams. Frontiers in Applied Mathematics and Statistics, 4, 41. doi:10.3389/fams.2018.00041
• Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z.-H., Steinbach, M., Hand, D. J., & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37. doi:10.1007/s10115-007-0114-2
• Yalcin, F., & Simsek, Y. (2020). A New Class of Symmetric Beta Type Distributions Constructed by Means of Symmetric Bernstein Type Basis Functions. Symmetry, 12(5), 779. doi:10.3390/sym12050779
• Zhang, L., Jiang, L., Li, C., & Kong, G. (2016). Two feature weighting approaches for naive bayes text classifiers. Knowledge-Based Systems, 100, 137–144.
• Zhao, Z., Zhao, J., Sano, Y., Levy, O., Takayasu, H., Takayasu, M., Li, D., Wu, J., & Havlin, S. (2020). Fake news propagates differently from real news even at early stages of spreading. EPJ Data Science, 9(1), 7. doi:10.1140/epjds/s13688-020-00224-z