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Çevrimiçi sosyal medyada sahte haber tespiti

Year 2020, Volume: 11 Issue: 1, 91 - 103, 27.03.2020
https://doi.org/10.24012/dumf.629368

Abstract

Son yıllarda, sosyal medyadan haber almak, bilginin hızlı bir şekilde yayılması, ucuz maliyet ve kolay erişim nedeniyle giderek daha popüler hale gelmiştir. Sosyal medyanın dünyadaki insanlar için temel bilgi kaynaklarından biri haline gelmesi toplum, kültür ve iş dünyası üzerinde olumlu ve olumsuz etkilere sahiptir. Sosyal medyadaki haberlerin kalitesi geleneksel haber kaynaklarından daha düşüktür ve sosyal medya sahte haber yaymak için çok uygundur. Sahte haberlerin insanlar ve toplum üzerindeki zararlı etkileri nedeniyle, sahte haberlerin tespiti dikkat çekmektedir. Bu çalışmada, sahte haberleri tespit etmek için iki aşamalı bir model önerilmiştir. İlk adımda, yapılandırılmamış verileri yapılandırılmış verilere dönüştürmek için sahte haberler içeren verilere bir dizi ön işlem uygulanmıştır. Bir sonraki adımda, yapılandırılmış sahte haber veri setine on denetimli yapay zekâ algoritması uygulanmıştır. Önerilen model dört farklı eğitim - test bölümlemesi ile incelenmiştir. Erişime açık veri seti üzerinde; Naive Bayes, JRip, J48, Rastgele Orman, Stokastik Gradyan İnişi, Yerel Ağırlıklı Öğrenme, Naive Bayes ile Karar Ağacı, Yerine Koyarak Öğrenme, Regresyon ile Sınıflandırma denetimli yapay zekâ algoritmaları test edilmiş ve bu algoritmalar üç değerlendirme ölçütüne bağlı olarak karşılaştırılmıştır.

References

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  • Vaishali, V., Bhalodiya, N., N.N Jani, N. N., (2014). Applying Naïve bayes, BayesNet, PART, JRip and OneR Algorithms on Hypothyroid Database for Comparative Analysis, International Journal of Darshan Institute on Engineering Research & Emerging Technologies, 3(1).
  • Vedova, M. D., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., de Alfaro, L., (2018). Automatic Online Fake News Detection Combining Content and Social Signals. 22st Conference of Open Innovations Association, 272-279.
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Year 2020, Volume: 11 Issue: 1, 91 - 103, 27.03.2020
https://doi.org/10.24012/dumf.629368

Abstract

References

  • Ahmed, H., Traore, I., Saad, S., (2017). Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. 127-138.
  • Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., Kochut, K., (2017). A brief survey of text mining: Classification, clustering and extraction techniques. arXiv preprint arXiv:1707.02919.
  • Breiman, L., (1996). Bagging Predictors. Machine Learning, 24(2), 123-140.
  • Corney, D., Albakour, D., Martinez, M., and S. Moussa, S., (2016). What do a million news articles look like?. First International Workshop on Recent Trends in News Information Retrieval co-located with 38th European Conference on Information Retrieval, Italy, 42–47.
  • Coşkun, C., Baykal, A., (2011). An Application for Comparison of Data Mining Classification Algorithms. Akademik Bilişim, 1-8.
  • Fong, S., Luo, Z., Yap, B. W., (2013). Incremental learning algorithms for fast classification in data stream. 2013 International Symposium on Computational and Business Intelligence, India, 186-190.
  • Frank, E., I. Witten, I., (1998). Generating Accurate Rule Sets Without Global Optimization. Fifteenth International Conference on Machine Learning, San Francisco.
  • Gilda, S., (2017). Evaluating Machine Learning Algorithms for Fake News Detection. 15th Student Conference on Research and Development, Malaysia, 110-115.
  • Granik, M., Mesyura, V., (2017). Fake news detection using naive Bayes classifier. First Ukraine Conference on Electrical and Computer Engineering, Ukranie, 900-903.
  • Guo, C., Cao, J., Zhang, X., Shu, K., Yu, M., Exploiting emotions for fake news detection on social media, arXiv preprint arXiv:1903.01728, 2019.
  • Hido, S., Kashima, H., Takahashi, Y., (2009). Roughly balanced bagging for imbalanced data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2, 412-426.
  • Ho, T. K., (1995). Random decision forests. 3rd International Conference on Document Analysis and Recognition, Canada, 1, 278-282.
  • Holte, R. C., (1993). Very simple classification rules perform well on most commonly used data sets, Machine Learning, 11, 63-90.
  • Khan, R., Hanbury, A., Stoettinger, J., (2010). Skin detection: A random forest approach. IEEE International Conference on Image Processing, China, pp. 4613-4616.
  • Kumar, L., Bhatia, P. K., (2013). Text Mining: concepts, process and applications. International Journal of Global Research in Computer Science, 4(3), 36-39.
  • Long, Y., Lu, Q., Xiang, R., Li, M., Huang, C. R., (2017). Fake news detection through multi-perspective speaker profiles. Eighth International Joint Conference on Natural Language Processing, Taiwan, 2, 252-256.
  • Mahajan, A., Ganpati, A., (2014). Performance evaluation of rule based classification algorithms. International Journal of Advanced Research in Computer Engineering & Technology, 3(10), 3546-3550.
  • Monti, F., Frasca, F., Eynard, D., Mannion, D., Bronstein, M. M., (2019). Fake news detection on social media using geometric deep learning, arXiv preprint arXiv:1902.06673, 2019.
  • Ozbay, F. A., Alatas, B., (2019). A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms. Elektronika ir Elektrotechnika, 25(4), 62-67.
  • Quinlan JR., (1992). Learning with continuous classes. 5th Australian Joint Conference on Artificial Intelligence, 92, 343–348.
  • Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., Choi, Y., (2017). Truth of varying shades: Analyzing language in fake news and political fact-checking. 2017 Conference on Empirical Methods in Natural Language Processing, Denmark, 2931-2937.
  • Rubin, V., Conroy, N., Chen, Y., & Cornwell, S., (2016). Fake news or truth? using satirical cues to detect potentially misleading news. Second Workshop on Computational Approaches to Deception Detection, California, 7-17.
  • Ruchansky, N., Seo, S., Liu, Y., (2017). Csi: A hybrid deep model for fake news detection. 2017 ACM on Conference on Information and Knowledge Management, Singapore, 797-806.
  • Ruder, S., (2016). An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747, 2016.
  • Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H., (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19, 22-36.
  • Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., Krause, A., (2018). Fake news detection in social networks via crowd signals. In Companion of the The Web Conference 2018 on The Web Conference 2018, 517-524.
  • Vaishali, V., Bhalodiya, N., N.N Jani, N. N., (2014). Applying Naïve bayes, BayesNet, PART, JRip and OneR Algorithms on Hypothyroid Database for Comparative Analysis, International Journal of Darshan Institute on Engineering Research & Emerging Technologies, 3(1).
  • Vedova, M. D., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., de Alfaro, L., (2018). Automatic Online Fake News Detection Combining Content and Social Signals. 22st Conference of Open Innovations Association, 272-279.
  • Zhang, J., Cui, L., Fu, Y., Gouza, F. B., (2018). Fake news detection with deep diffusive network model. arXiv preprint arXiv:1805.08751.
There are 29 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Feyza Altunbey Özbay 0000-0003-0629-6888

Bilal Alataş 0000-0002-3513-0329

Publication Date March 27, 2020
Submission Date October 4, 2019
Published in Issue Year 2020 Volume: 11 Issue: 1

Cite

IEEE F. Altunbey Özbay and B. Alataş, “Çevrimiçi sosyal medyada sahte haber tespiti”, DUJE, vol. 11, no. 1, pp. 91–103, 2020, doi: 10.24012/dumf.629368.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456