FAKE NEWS DETECTION WITH LSTM AND BERT MODELS

Öz

In the digitalised world, information pollution has become a major problem with the ease of access to information. The potential of social media platforms to reach large masses and the ability of users to produce content in an uncontrolled manner have further facilitated the spread of fake news. In particular, the rapid spread of unconfirmed content may cause individuals to be misinformed and social perceptions to be manipulated. Such news has a level of influence that can affect the behaviour of individuals and public opinion. The spread of fake news not only undermines individuals' trust in information, but also paves the way for social problems such as polarisation, panic and misdirection in society. Therefore, early detection of fake news is of great importance to prevent these problems. Developing artificial intelligence and natural language processing techniques offer effective solutions to this problem. In this study, LSTM and BERT models are used for fake news detection. In the study, two data sets consisting of real and fake news were combined in a balanced way and turned into a single data set. In this data set, 80% of the data were used as training data and 20% as test data. While 93% success was achieved with the LSTM model, 98% success was achieved with the BERT model.

Anahtar Kelimeler

• Fake news detection
• LSTM
• BERT

LSTM VE BERT MODELLERİ İLE SAHTE HABER TESPİTİ

Öz

Dijitalleşen dünyada bilgiye erişimin kolaylaşması ile birlikte bilgi kirliliği büyük bir sorun haline gelmiştir. Sosyal medya platformlarının geniş kitlelere ulaşma potansiyeli ve kullanıcılar tarafından denetimsiz bir şekilde içerik üretilebilmesi, sahte haberlerin yayılımını daha da kolaylaştırmıştır. Özellikle doğruluğu teyit edilmemiş içeriklerin hızla yayılması, bireylerin yanlış bilgilendirilmesine ve toplumsal algıların manipüle edilmesine neden olabilmektedir. Bu tür haberler; bireylerin davranışlarını, kamuoyunu etkileyebilecek bir etki düzeyine sahiptir. Sahte haberlerin yayılması yalnızca bireylerin bilgiye olan güvenini sarsmakla kalmayıp aynı zamanda toplumda kutuplaşma, panik ve yanlış yönlendirme gibi sosyal sorunlara da zemin hazırlamaktadır. Bu nedenle, sahte haberlerin erken tespiti bu sorunların önüne geçilebilmesi için büyük önem taşımaktadır. Gelişen yapay zekâ ve doğal dil işleme teknikleri, bu sorunun tespitine yönelik etkili çözümler sunmaktadır. Bu çalışmada sahte haber tespiti için LSTM ve BERT modelleri kullanılmıştır. Çalışmada gerçek ve sahte haberlerden oluşan iki veri seti dengeli bir şekilde birleştirilerek tek veri setine haline getirilmiştir. Bu veri setinde bulunan verilerin %80’i eğitim ve %20’si test verileri olarak kullanılmıştır. LSTM modeli ile %93 oranında bir başarı elde edilirken BERT modeli ile bu başarı %98 olarak elde edilmiştir.

Anahtar Kelimeler

• Sahte haber tespiti
• LSTM
• BERT

Kaynakça

1. M. Öztunç, O. Kartav, Sosyal medyada yalan haber sorunu ve doğrulama platformları
2. H. Allcott, M. Gentzkow, Social media and fake news in the 2016 election, Journal of Economic Perspectives 31 (2017) 211–236.
3. Ünal, R., Taylan, A., Sağlık iletişiminde yalan haber–yanlış enformasyon sorunu ve doğrulama platformları, Atatürk İletişim Dergisi 14 (2017) 81–100.
4. M.G. Samuels, Review: The filter bubble: What the internet is hiding from you by Eli Pariser, InterActions: UCLA Journal of Education and Information Studies 8 (2012).
5. D. Spohr, Fake news and ideological polarization: Filter bubbles and selective exposure on social media, Business Information Review 34 (2017) 150–160.
6. K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake news detection on social media: A data mining perspective, SIGKDD Explorations 19 (2017) 22–36.
7. K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake news detection on social media, ACM SIGKDD Explorations Newsletter 19 (2017) 22–36.
8. X. Zhou, R. Zafarani, A survey of fake news: Fundamental theories, detection methods, and opportunities, ACM Computing Surveys 53 (2020) 1–40.

9. H. Rashkin, E. Choi, J.Y. Jang, S. Volkova, Y. Choi, Truth of varying shades: Analyzing language in fake news and political fact-checking, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017) 2931–2937.
10. J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, arXiv:1810.04805 (2018).
11. J. Li, H. Shi, S. Tang, F. Wu, Y. Zhuang, Informative visual storytelling with cross-modal rules, in: Proceedings of the 27th ACM International Conference on Multimedia, ACM, 2019, pp. 2314–2322.
12. Z. Jin, J. Cao, H. Guo, Y. Zhang, J. Luo, Multimodal fusion with recurrent neural networks for rumor detection on microblogs, in: Proceedings of the ACM Multimedia Conference, ACM, 2017, pp. 795–816.
13. K. Shu, S. Wang, H. Liu, Beyond news contents: The role of social context for fake news detection, in: Proceedings of the 12th ACM International Conference on Web Search and Data Mining, ACM, 2019, pp. 312–320.
14. H. Ahmed, I. Traore, S. Saad, Detecting opinion spams and fake news using text classification, Security and Privacy 1 (2018).
15. N. Ruchansky, S. Seo, Y. Liu, CSI: A hybrid deep model for fake news detection, in: Proceedings of the ACM Conference on Information and Knowledge Management, ACM, 2017, pp. 797–806.
16. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, V. Stoyanov, RoBERTa: A robustly optimized BERT pretraining approach, arXiv:1907.11692 (2019).
17. R.K. Kaliyar, A. Goswami, P. Narang, FakeBERT: Fake news detection in social media with a BERT-based deep learning approach, Multimedia Tools and Applications 80 (2021) 11765–11788.
18. F. Monti, F. Frasca, D. Eynard, D. Mannion, M.M. Bronstein, Fake news detection on social media using geometric deep learning, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (2019).
19. A. Yadav, S. Gaba, H. Khan, I. Budhiraja, A. Singh, K.K. Singh, ETMA: Efficient transformer-based multilevel attention framework for multimodal fake news detection, yayımlanma bilgisi eklenmelidir.
20. L. Wang, C. Zhang, H. Xu, Y. Xu, X. Xu, S. Wang, Cross-modal contrastive learning for multimodal fake news detection, in: Proceedings of the 31st ACM International Conference on Multimedia, ACM, 2023, pp. 5696–5704.
21. S. Suryavardan, S. Mishra, M. Chakraborty, P. Patwa, A. Rani, A. Chadha, A. Reganti, A. Das, A. Sheth, M. Chinnakotla, A. Ekbal, S. Kumar, Findings of Factify 2: Multimodal fake news detection, CLEF Working Notes (2023).
22. C.-O. Truică, E.-S. Apostol, MisRoBÆRTa: Transformers versus misinformation, Mathematics 10 (2023).
23. J.A. Nasir, O.S. Khan, I. Varlamis, Fake news detection: A hybrid CNN–RNN based deep learning approach, International Journal of Information Management Data Insights 1 (2021).
24. S. Fitria, N. Azizah, H.D. Cahyono, S.W. Sihwi, W. Widiarto, Performance analysis of transformer-based models (BERT, ALBERT and RoBERTa) in fake news detection, yayımlanma bilgisi eklenmelidir.
25. L. Al-Zahrani, M. Al-Yahya, Pre-trained language model ensemble for Arabic fake news detection, Mathematics 12 (2024).
26. S. Raza, D. Paulen-Patterson, C. Ding, Fake news detection: Comparative evaluation of BERT-like models and large language models with generative AI-annotated data, arXiv:2412.14276 (2024).
27. I.Q. Abduljaleel, I.H. Ali, Detecting fake news using BERT word embedding, attention mechanism, partition and overlapping text techniques, TEM Journal 14 (2025) 1152–1165.
28. A. Mukherjee, S. Ghosh, UNITE-FND: Reframing multimodal fake news detection through unimodal scene translation, arXiv:2502.11132 (2025).
29. İ. Kulaksız, A. Coşkunçay, Fake news detection on mainstream media using natural language processing, Black Sea Journal of Engineering and Science 8 (2025) 214–224.
30. X. Xu, P. Yu, Z. Xu, J. Wang, A hybrid attention framework for fake news detection with large language models, arXiv:2501.11967 (2025).
31. W.Y. Wang, “Liar, liar pants on fire”: A new benchmark dataset for fake news detection, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL, 2017, pp. 422–426.
32. J. Thorne, A. Vlachos, C. Christodoulopoulos, A. Mittal, FEVER: A large-scale dataset for fact extraction and verification, in: Proceedings of NAACL-HLT, 2018.
33. P. Patwa, S. Sharma, S. Pykl, V. Guptha, G. Kumari, S. Akhtar, A. Ekbal, A. Das, T.Chakraborty, Fighting an infodemic: COVID-19 fake news dataset, Springer Lecture Notes in Computer Science (2021).
34. L. Derczynski, K. Bontcheva, M. Lukasik, T. Declerck, A. Scharl, G. Georgiev, P. Osenova, P. Lobo, A. Kolliakou, R. Stewart, S.-J. Terp, G. Wong, C. Burger, A. Zubiaga, R. Procter, PHEME: Computing veracity in social media, ACM Transactions on Social Computing (2017).
35. T. Mitra, E. Gilbert, CREDBANK: A large-scale social media corpus with associated credibility annotations, Proceedings of the 24th International World Wide Web Conference (2015).
36. J. Ma, W. Gao, P. Mitra, S. Kwon, B.J. Jansen, K.-F. Wong, M. Cha, Detecting rumors from microblogs with recurrent neural networks, Proceedings of IJCAI (2016).
37. B.D. Horne, S. Adalı, This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, Proceedings of ICWSM (2017).
38. K. Shu, D. Mahudeswaran, S. Wang, D. Lee, H. Liu, FakeNewsNet: A data repository with news content, social context and spatiotemporal information for studying fake news on social media, arXiv:1809.01286 (2019).
39. F. Farhangian, R.M.O. Cruz, G.D.C. Cavalcanti, Fake news detection: Taxonomy and comparative study, Information Fusion 103 (2024).
40. K. Nakamura, S. Levy, W.Y. Wang, r/Fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection, in: Proceedings of LREC (2020).
41. C. Boididou, K. Andreadou, S. Papadopoulos, D.-T. Dang-Nguyen, G. Boato, M. Riegler, Y. Kompatsiaris, Verifying multimedia use at MediaEval 2015, CEUR Workshop Proceedings (2015).
42. T. Bolukbasi, K.-W. Chang, J. Zou, V. Saligrama, A. Kalai, Man is to computer programmer as woman is to homemaker? Debiasing word embeddings, Advances in Neural Information Processing Systems (2016).
43. Z. Zhou, H. Guan, M.M. Bhat, J. Hsu, Fake news detection via natural language processing is vulnerable to adversarial attacks, arXiv (2020).
44. Kaggle, Fake news detection datasets, https://www.kaggle.com/datasets/emineyetm/fake-news-detection-datasets (accessed 10 March 2025).
45. U. Ergün, S. Orcin, S. Barın, Extraction of clinical entities from chest radiology reports using NLP methods, Journal of Materials and Mechatronics 6 (2025) 1–14.
46. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (1997) 1735–1780.
47. X.H. Le, H.V. Ho, G. Lee, S. Jung, Application of long short-term memory neural network for flood forecasting, Water 11 (2019).
48. A.P.M. Diniz, P.M. Ciarelli, E.O.T. Salles, K.F. Coco, Use of deep neural networks for clogging detection in the submerged entry nozzle of the continuous casting, Expert Systems with Applications 238 (2024).
49. M. Khadhraoui, H. Bellaaj, M. Ben Ammar, H. Hamam, M. Jmaiel, Survey of BERT-base models for scientific text classification: COVID-19 case study, Applied Sciences 12 (2022).
50. A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, S.R. Bowman, SuperGLUE: A stickier benchmark for general-purpose language understanding systems, Advances in Neural Information Processing Systems (2019).
51. X. Zhang, J. Fan, M. Hei, Compressing BERT for binary text classification via adaptive truncation before fine-tuning, Applied Sciences 12 (2022).
52. S. Orozco-Arias, G. Isaza, R. Guyot, R. Tabares-Soto, A systematic review of the application of machine learning in the detection and classification of transposable elements, PeerJ 7 (2019).
53. S. Orozco-Arias, J.S. Piña, R. Tabares-Soto, L.F. Castillo-Ossa, R. Guyot, G. Isaza, Measuring performance metrics of machine learning algorithms for detecting and classifying transposable elements, Processes 8 (2020).
54. M.A. Karabıyık, Kontrollü dengesizlik senaryolarında topluluk öğrenme modellerinin sistematik karşılaştırması, Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi 9 (2025) 41–50.
55. R. Izhar, S.N. Bhatti, S.A. Alharthi, Bridging precision and complexity: A novel machine learning approach for ambiguity detection in software requirements, IEEE Access 13 (2025) 12014–12031.
56. S.P. Taş, S. Barin, G.E. Güraksin, Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture, Acta Scientiarum Technology 44 (2022).