Detection of Fake News Published on the Internet Using Artificial Intelligence Based Natural Language Processing Approach
Abstract
Fake news is fabricated news that spread consciously or unconsciously through various communication channels and has no real share. Today, the masses receive most news on digital and social media. In such communication environments, where news can be transferred to the masses quickly, the accuracy of this news can often be abused. News of unknown origin can cause serious problems in societies by making disinformation or misinformation. Especially, fake news exposed to information pollution in the internet environment can show its effect on society very quickly. To prevent such problems in digital environments, an artificial intelligence-based approach that can grasp the accuracy of the news and confirm it quickly is proposed in this study. In addition, a classification analysis was performed using the Natural Language Processing (NLP) method, a sub-branch of artificial intelligence, to determine whether the news was real or false using the dataset that was accessible. The dataset consisted of 6335 news headlines and content. While 3171 of this news is real news; 3164 is fake news. In the analysis of the study, the Long Short Term Memory (LSTM) model was used together with the NLP method and the training of the dataset was carried out with this model. As a result, the overall accuracy success from the training data was 99.83%, and the overall accuracy success from the test data was 91.48%. These results show us that similar studies that we plan to think about in the future have been promising.
Keywords
Natural Language Processing Artificial Intelligence Fake News Deep Learning News Classification
References
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- Chest X-Ray Images.”, Applied Soft Computing, 103: 107160. https://doi.org/https://doi.org/10.1016/j.asoc.2021.107160.
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- Kaliyar, Rohit K., Anurag G., Pratik N., ve Soumendu S., 2020, “FNDNet – A Deep Convolutional Neural Network for Fake News Detection.”, Cognitive Systems Research, 61: 32–44. https://doi.org/https://doi.org/10.1016/j.cogsys.2019.12.005.
- Le, Xuan H., Hung Viet H., Giha L., ve Sungho J., 2019, “Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting.”, Water (Switzerlve), 11 (7). https://doi.org/10.3390/w11071387.
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- McIntire, G., 2017, “Machine Learning Finds ‘Fake News’ with 88% Accuracy.” KD Nuggets, 2017, https://www.kdnuggets.com/2017/04/machine-learning-fake-news-accuracy.html.
- Ong, Charlene J., Agni O., Rebecca Z., Francois Pierre M. C., Meghan H., Liang M., Darian F., vd., 2020, “Machine Learning ve Natural Language Processing Methods to Identify Ischemic Stroke, Acuity ve Location from Radiology Reports.”, PLOS ONE, 15 (6): e0234908. https://doi.org/10.1371/journal.pone.0234908.
- Onan A., 2021, “Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach.”, Comput. Appl. Eng. Educ, 29: 572–589. doi:10.1002/cae.22253.
- Pennington, J., Socher R., ve Manning C.D., 2015, “GloVe: Global Vectors for Word Representation.” Stanford University, 2015, https://nlp.stanford.edu/projects/glove/.
- Jayaseelan R., Brindha D., ve Kades W, 2020, “Social Media Reigned by Information or Misinformation About COVID-19: A Phenomenological Study.”, SSRN Electronic Journal, https://doi.org/10.2139/ssrn.3596058.
- Sreekumar D., ve Chitturi B., 2020, “Deep Neural Approach to Fake-News Identification.”, Procedia Computer Science, 167: 2236–43, https://doi.org/https://doi.org/10.1016/j.procs.2020.03.276.
- Sertkaya, M. E., Ergen B., ve Togacar M., 2019, “Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images.”, In 2019 23rd International Conference Electronics, 1–5. https://doi.org/10.1109/electronics.2019.8765579.
- Sun, Shaojing, Yujia Zhai, Bin Shen, ve Yibei Chen. 2020. “Newspaper Coverage of Artificial Intelligence: A Perspective of Emerging Technologies.” Telematics ve Informatics, 101433. https://doi.org/https://doi.org/10.1016/j.tele.2020.101433.
- Ünal, R., ve Taylan A., 2017, “Sağlık İletişiminde Yalan Haber - Yanlış Enformasyon Sorunu ve Doğrulama Platformları.”, Atatürk İletişim Dergisi / Dergi Park. https://dergipark.org.tr/tr/pub/atauniiletisim/issue/34005/360148.
- Wang, Y., Li Y., Yong S., ve Rong X., 2020, “The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition.”, Applied Sciences (Switzerlve), 10 (5). https://doi.org/10.3390/app10051897.
- Yang, Z., Wang C., Zhang Z., ve Li J., 2019, “Mini-Batch Algorithms with Online Step Size.” Knowledge-Based Systems, 165: 228–40. https://doi.org/10.1016/j.knosys.2018.11.031.
- Yüksel A.S., Tan F.G., 2018, “A real-time social network-based knowledge discovery system for decision making”, Automatika., 59: 261–273. https://doi.org/10.1080/00051144.2018.1531214.
- Zhou, M., Nan D., Shujie L., ve Heung-Yeung S., 2020, “Progress in Neural NLP: Modeling, Learning, ve Reasoning.”, Engineering, 6 (3): 275–90. https://doi.org/10.1016/j.eng.2019.12.014.
Yapay Zekâ Tabanlı Doğal Dil İşleme Yaklaşımını Kullanarak İnternet Ortamında Yayınlanmış Sahte Haberlerin Tespiti
Abstract
Sahte haber, bilinçli veya bilinçsiz bir şekilde çeşitli iletişim kanallarını kullanarak yayılan ve hiç bir gerçeklik payı olmayan uydurma haberlerdir. Günümüzde kitleler çoğu haberleri dijital ve sosyal medya üzerinden alıyorlar. Haberlerin hızlı bir şekilde kitlelere aktarabildiği bu tür iletişim ortamlarında çoğu zaman bu haberlerin doğruluğu suiistimal edinilebiliyor. Kökeni bilinmeyen haberler dezenformasyon veya yanlış bilgilendirme yapılarak toplumlarda ciddi sorunlar oluşturabilmektedir. Özellikle internet ortamında bilgi kirliliğine maruz kalan sahte haberler çok hızlı bir şekilde topluma etkisini gösterebilmektedir. Dijital ortamlarda bu tür problemlerin önüne geçilebilmesi için haberlerin doğruluğunu kavrayabilen ve hızlı bir şekilde teyit eden yapay zekâ tabanlı bir yaklaşım bu çalışmada önerilmektedir. Ayrıca, yapay zekânın bir alt dalı olan Doğal Dil İşleme (DDİ) yöntemi ile erişime açık veri setini kullanarak haberlerin gerçek veya sahte olduğunu tespit eden sınıflandırma analizi gerçekleştirildi. Veri seti, 6335 haber başlığı ve içerikten oluşmaktadır. Bu haberlerin 3171'i gerçek haber niteliği taşırken; 3164'ü ise sahte haber niteliği taşımaktadır. Çalışmanın analizinde DDİ yöntemi ile birlikte Uzun Kısa Süreli Bellek (UKSB) modeli kullanıldı ve veri setinin eğitimi bu model sayesinde gerçekleştirildi. Sonuç olarak, bu çalışmada eğitim verilerinden elde edilen genel doğruluk başarısı % 99,83 idi ve test verilerinden elde edilen genel doğruluk başarısı % 91,48 idi. Bu sonuçlar bize gösteriyor ki gelecekte düşünmeyi planladığımız benzeri çalışmalara umut verici olmuştur.
References
- Adalı, E., 2016, “Doğal Dil İşleme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi , 5 (2). https://dergipark.org.tr/tr/pub/tbbmd/issue/22245/238797
- Altunbey Özbay, F., ve Alataş B, 2020, “Çevrimiçi Sosyal Medyada Sahte Haber Tespiti.”, DÜMF Mühendislik Dergisi, 11 (1): 91–103. https://doi.org/10.24012/dumf.629368.
- Atenstaedt, R., 2012, “Word Cloud Analysis of the BJGP.”, British Journal of General Practice, 62 (596): 148 LP – 148, https://doi.org/10.3399/bjgp12X630142.
- Bock, S., ve Weiß M., 2019, “A Proof of Local Convergence for the Adam Optimizer.”, In 2019 International Joint Conference on Neural Networks (IJCNN), 1–8, https://doi.org/10.1109/ijcnn.2019.8852239.
- Chitic, R., 2021, “REAL ve FAKE News Dataset.” Kaggle, 2021, https://www.kaggle.com/rchitic17/real-or-fake. Demir, F., 2021, “DeepCoroNet: A Deep LSTM Approach for Automated Detection of COVID-19 Cases from
- Chest X-Ray Images.”, Applied Soft Computing, 103: 107160. https://doi.org/https://doi.org/10.1016/j.asoc.2021.107160.
- Doğan, F., ve Türkoğlu İ., 2019, “Derin Öğrenme Modelleri ve Uygulama Alanlarına İlişkin Bir Derleme”, DÜMF Mühendislik Dergisi 10 (2): 409–45, https://doi.org/10.24012/dumf.411130.
- Figdor, C., 2017. “(When) Is Science Reporting Ethical? The Case for Recognizing Shared Epistemic Responsibility in Science Journalism.”, Frontiers in Communication, 2: 3, https://doi.org/10.3389/fcomm.2017.00003.
- Horne, Benjamin D., ve Adali S., 2017, “This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News.”, http://arxiv.org/abs/1703.09398.
- Kaliyar, Rohit K., Anurag G., Pratik N., ve Soumendu S., 2020, “FNDNet – A Deep Convolutional Neural Network for Fake News Detection.”, Cognitive Systems Research, 61: 32–44. https://doi.org/https://doi.org/10.1016/j.cogsys.2019.12.005.
- Le, Xuan H., Hung Viet H., Giha L., ve Sungho J., 2019, “Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting.”, Water (Switzerlve), 11 (7). https://doi.org/10.3390/w11071387.
- Madz, 2021, “NLP Using GloVe Embeddings.” Kaggle, 2021, https://www.kaggle.com/madz2000/nlp-using-glove-embeddings-99-8-accuracy.
- McIntire, G., 2017, “Machine Learning Finds ‘Fake News’ with 88% Accuracy.” KD Nuggets, 2017, https://www.kdnuggets.com/2017/04/machine-learning-fake-news-accuracy.html.
- Ong, Charlene J., Agni O., Rebecca Z., Francois Pierre M. C., Meghan H., Liang M., Darian F., vd., 2020, “Machine Learning ve Natural Language Processing Methods to Identify Ischemic Stroke, Acuity ve Location from Radiology Reports.”, PLOS ONE, 15 (6): e0234908. https://doi.org/10.1371/journal.pone.0234908.
- Onan A., 2021, “Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach.”, Comput. Appl. Eng. Educ, 29: 572–589. doi:10.1002/cae.22253.
- Pennington, J., Socher R., ve Manning C.D., 2015, “GloVe: Global Vectors for Word Representation.” Stanford University, 2015, https://nlp.stanford.edu/projects/glove/.
- Jayaseelan R., Brindha D., ve Kades W, 2020, “Social Media Reigned by Information or Misinformation About COVID-19: A Phenomenological Study.”, SSRN Electronic Journal, https://doi.org/10.2139/ssrn.3596058.
- Sreekumar D., ve Chitturi B., 2020, “Deep Neural Approach to Fake-News Identification.”, Procedia Computer Science, 167: 2236–43, https://doi.org/https://doi.org/10.1016/j.procs.2020.03.276.
- Sertkaya, M. E., Ergen B., ve Togacar M., 2019, “Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images.”, In 2019 23rd International Conference Electronics, 1–5. https://doi.org/10.1109/electronics.2019.8765579.
- Sun, Shaojing, Yujia Zhai, Bin Shen, ve Yibei Chen. 2020. “Newspaper Coverage of Artificial Intelligence: A Perspective of Emerging Technologies.” Telematics ve Informatics, 101433. https://doi.org/https://doi.org/10.1016/j.tele.2020.101433.
- Ünal, R., ve Taylan A., 2017, “Sağlık İletişiminde Yalan Haber - Yanlış Enformasyon Sorunu ve Doğrulama Platformları.”, Atatürk İletişim Dergisi / Dergi Park. https://dergipark.org.tr/tr/pub/atauniiletisim/issue/34005/360148.
- Wang, Y., Li Y., Yong S., ve Rong X., 2020, “The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition.”, Applied Sciences (Switzerlve), 10 (5). https://doi.org/10.3390/app10051897.
- Yang, Z., Wang C., Zhang Z., ve Li J., 2019, “Mini-Batch Algorithms with Online Step Size.” Knowledge-Based Systems, 165: 228–40. https://doi.org/10.1016/j.knosys.2018.11.031.
- Yüksel A.S., Tan F.G., 2018, “A real-time social network-based knowledge discovery system for decision making”, Automatika., 59: 261–273. https://doi.org/10.1080/00051144.2018.1531214.
- Zhou, M., Nan D., Shujie L., ve Heung-Yeung S., 2020, “Progress in Neural NLP: Modeling, Learning, ve Reasoning.”, Engineering, 6 (3): 275–90. https://doi.org/10.1016/j.eng.2019.12.014.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | March 2, 2022 |
| Submission Date | June 10, 2021 |
| Published in Issue | Year 2022 Volume: 5 Issue: 1 |
Cite
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