AN ENSEMBLE-BASED FRAMEWORK FOR DETECTING FAKE NEWS AND MITIGATING DIGITAL MISINFORMATION

Abdillah, A., Putra, C. B. P., Apriantoni, A., Juanita, S., & Purwitasari, D. (2022). Ensemble-based methods for multi-label classification on biomedical question-answer data. Journal of Information Systems Engineering and Business Intelligence. DOI: 10.20473/jisebi.8.1.42-50

Abedalla, A., Al-Sadi, A., and Abdullah, M. (2019). A Closer Look at Fake News Detection: A Deep Learning Perspective. Proceedings of the 3rd International Conference on Advances in Artificial Intelligence. DOI: 10.1145/3369114.3369149

Al-Tai, M. H., Nema, B. M., and Al-Sherbaz, A. (2022). Deep Learning for Fake News Detection: Literature Review. Al-Mustansiriyah Journal of Science. DOI: 10.23851/mjs.v34i2.1292

Algabri, M., Huliqah, E. N. A., and Ghurab, M. (2024). Fake News Detection On Social Media: Review of Literature. DOI: 10.59628/jast.v2i1.369.

Alghamdi, J., Lin, Y., and Luo, S. (2022). A comparative study of machine learning and deep learning techniques for fake news detection. Information, 13(12), 576. DOI: 10.3390/info13120576

Ali, A. M., Ghaleb, F. A., & Al-Rimy, B. (2022). Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique. Sensors, 22(18). DOI: 10.3390/s22186970

Ali, I., Ayub, M. N. B., Shivakumara, P., and Noor, N. F. B. M. (2022). Fake News Detection Techniques on Social Media: A Survey. Wireless Communications and Mobile Computing. DOI: 10.1155/2022/6072084

Alkomah, B., and Sheldon, F. (2023). Advancements in Fake News Detection Using Machine Learning Models. DOI: 10.1109/CSCI62032.2023.00142.

Athira A, B., Kumar, S. D. M., and Chacko, A. (2022). Towards Smart Fake News Detection Through Explainable AI. ArXiv. DOI: 10.48550/arXiv.2207.11490

J. Huete, A. A. Qahtan, and M. Hassani, “PrefixCDD: Effective online concept drift detection over event streams using prefix trees,” in Proc. IEEE 47th Annu. Comput., Softw., Appl. Conf. (COMPSAC), 2023, pp. 328–333, doi: 10.1109/COMPSAC57700.2023.00051.

Barushka, A., and Hájek, P. (2019). The Effect of Text Preprocessing Strategies on Detecting Fake Consumer Reviews. Proceedings of the 2019 3rd International Conference on E-Business and Internet. DOI: 10.1145/3383902.3383908

Belete, D., and Huchaiah, M. D. (2021). Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results. International Journal of Computers and Applications, 44, 875–886. DOI: 10.1080/1206212X.2021.1974663

Bensouda, N., El Fkihi, S., and Faizi, R. (2024). A Novel Ensemble Model for Detecting Fake News. IAES International Journal of Artificial Intelligence (IJ-AI), 13(1). DOI: 10.11591/ijai.v13.i1.pp1160-1171

Bondielli, A., and Marcelloni, F. (2019). A Survey on Fake News and Rumour Detection Techniques. Information Sciences, 497, 38–55. DOI: 10.1016/j.ins.2019.05.035

Chai, C. P. (2022). Comparison of Text Preprocessing Methods. Natural Language Engineering. DOI: 10.1017/S1351324922000213

Crestani, F., & Rosso, P. (2020). The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers. In Natural Language Processing and Information Systems (pp. 181–194). Springer. DOI: 10.1007/978-3-030-51310-8_14

Cueva, E., Ee, G., Iyer, A., et al. (2020). Detecting Fake News on Twitter Using Machine Learning Models. IEEE MIT Undergraduate Research Technology Conference, 1–5. DOI: 10.1109/urtc51696.2020.9668872

David, M. S., & Renjith, S. (2021). Comparison of word embeddings in text classification based on RNN and CNN. IOP Conference Series: Materials Science and Engineering, 1187. DOI: 10.1088/1757-899X/1187/1/012029

Deepak, S., & Chitturi, B. (2020). Deep Neural Approach to Fake-News Identification. Procedia Computer Science, 167, 2236–2243. DOI: 10.1016/j.procs.2020.03.276

Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016). The Spreading of Misinformation Online. Proceedings of the National Academy of Sciences, 113(3), 554–559. DOI: 10.1073/pnas.1517441113

Deokate, B. J. (2021). Detecting Fake News Using Social Media Platforms. International Journal for Research in Applied Science and Engineering Technology, 9, 485–489. DOI: 10.22214/ijraset.2021.38561

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). DOI: 10.18653/v1/N19-1423.

Ding, J., Hu, Y., and Chang, H. (2020). BERT-based mental model, a better fake news detector. Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. DOI: 10.1145/3404555.3404607

Dogru, H. B., Tilki, S., Jamil, A., and Hameed, A. A. (2021). Deep Learning-Based Classification of News Texts Using Doc2Vec Model. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), 91–96. DOI: 10.1109/CAIDA51941.2021.9425290

Ghanem, B., Rosso, P., and Rangel, F. (2018). Stance Detection in Fake News: A Combined Feature Representation. In Proceedings of the First Workshop on Fact Extraction and Verification (FEVER) (pp. 66–71). DOI: 10.18653/v1/W18-5511

Girgis, S., and Amer, E. (2022). A Proposed Ensemble Voting Model for Fake News Detection. 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), 316–322. DOI: 10.1109/MIUCC55081.2022.9781788

Goldani, M. H., Momtazi, S., and Safabakhsh, R. (2020). Detecting Fake News with Capsule Neural Networks. Applied Soft Computing, 101, 106991, DOI: 10.1016/j.asoc.2020.106991

Goonathilake, M. D. P. P., and Kumara, P. P. N. V. (2023). Stance-based fake news identification on social media with hybrid CNN and RNN-LSTM models. International Journal on Advances in ICT for Emerging Regions (ICTer). DOI: 10.4038/icter.v16i3.7234

Gunasegaran, T., and Cheah, Y. (2017). Evolutionary cross-validation. 2017 8th International Conference on Information Technology (ICIT), 89–95. DOI: 10.1109/ICITECH.2017.8079960

Guo, Z. (2022). Forestry Text Classification Based on BERT and KNN. 2022 International Conference on Information Technology, Communication Ecosystem and Management (ITCEM), 61–65.

Han, W., & Mehta, V. (2019). Fake news detection in social networks using machine learning and deep learning: Performance evaluation. 2019 IEEE International Conference on Industrial Internet (ICII). DOI: 10.1109/ICII.2019.00070

Hansrajh, A., Adeliyi, T. T., and Wing, J. (2021). Detection of Online Fake News Using Blending Ensemble Learning. Scientific Programming, 2021, Article ID 3434458. DOI:

Huang, L. (2022). Deep Learning for Fake News Detection: Theories and Models. Proceedings of the 6th International Conference on Electronic Information Technology and Computer Engineering. DOI: 10.1145/3573428.3573663

Huang, L. (2023). Deep Learning for Fake News Detection: Theories and Models. Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering. DOI: 10.1145/3573428.3573663

Huang, L. (2023). Detecting Fake News With Deep Neural Networks. Applied and Computational Engineering. DOI: 10.54254/2755-2721/5/20230619

Huang, Q. (2023). Detecting Fake News With Deep Neural Networks. Applied and Computational Engineering. DOI: 10.54254/2755-2721/5/20230619

Huang, Y.-F., and Chen, P.-H. (2020). Fake News Detection Using an Ensemble Learning Model Based on Self-Adaptive Harmony Search Algorithms. Expert Systems with Applications, 159, 113584. DOI: 10.1016/j.eswa.2020.113584

Islam, M. R., Liu, S., Wang, X., and Xu, G. (2020). Deep Learning for Misinformation Detection on Online Social Networks: A Survey and New Perspectives. Social Network Analysis and Mining, 10. DOI: 10.1007/s13278-020-00696-x

Jain, A., and Kasbe, A. (2018). Fake News Detection. 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), 1–5. DOI: 10.1109/SCEECS.2018.8546944

Kaliyar, R. K., Goswami, A., Narang, P., and Chamola, V. (2022). Understanding the Use and Abuse of Social Media: Generalized Fake News Detection With a Multichannel Deep Neural Network. IEEE Transactions on Computational Social Systems. DOI: 10.1109/tcss.2022.3221811

Kaliyar, R. K., Goswami, A., Narang, P., and Sinha, S. (2020). FNDNet – A Deep Convolutional Neural Network for Fake News Detection. Cognitive Systems Research, 61, 32–44. DOI: 10.1016/j.cogsys.2019.12.005

Kanwar, C., & Mohan, Y. (2023). Fake News Detection Using Natural Language Processing and Machine Learning Techniques. 2023 Seventh International Conference on Image Information Processing (ICIIP). DOI: 10.1109/ICIIP61524.2023.10537640

Keya, A. J., Afridi, S., Maria, A. S., et al. (2021). Fake News Detection Based on Deep Learning. International Conference on Science and Contemporary Technologies (ICSCT), 1–6. DOI: 10.1109/ICSCT53883.2021.9642565

Kim, D.-W. (2023). Text Classification Based on Neural Network Fusion. Tehni?ki glasnik. DOI: 10.31803/tg-20221228154330

Kokkinos, Y., and Margaritis, K. (2018). Managing the computational cost of model selection and cross-validation in extreme learning machines via Cholesky, SVD, QR and eigen decompositions. Neurocomputing, 295, 29–45. DOI: 10.1016/j.neucom.2018.01.005

Kreš?áková, V. M., Sarnovský, M., and Butka, P. (2019). Deep Learning Methods for Fake News Detection. IEEE 19th International Symposium on Computational Intelligence and Informatics, 143–148. DOI: 10.1109/CINTI-MACRo49179.2019.9105317

Kumar, S., Asthana, R., Upadhyay, S., Upreti, N., and Akbar, M. (2019). Fake News Detection Using Deep Neural Networks. Transactions on Emerging Telecommunications Technologies, 31. DOI: 10.1002/ett.3767

Kuriyozov, E., Salaev, U., Matlatipov, S., and Matlatipov, G. (2023). Text classification dataset and analysis for Uzbek language. ArXiv. DOI: 10.48550/arXiv.2302.14494

Lee, D.-H., Kim, Y.-R., Kim, H.-J., Park, S.-M., and Yang, Y.-J. (2019). Fake News Detection Using Deep Learning. Journal of Information Processing Systems (JIPS, 15), 1119–1130. DOI: 10.3745/JIPS.04.0142

Li, M. (2023). Fake News Detection Using Deep Learning Approaches. 2023 4th International Symposium on Computer Engineering and Intelligent Communications, 233–238. DOI: 10.1109/ISCEIC59030.2023.10271110

Li, W., Gao, S., Zhou, H., Huang, Z., Zhang, K., and Li, W. (2019). The Automatic Text Classification Method Based on BERT and Feature Union. 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), 774–777. DOI: 10.1109/ICPADS47876.2019.00114

Lumumba, V., Kiprotich, D., and Mpaine, M. (2024). Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models. American Journal of Theoretical and Applied Statistics. DOI:10.11648/j.ajtas.20241305.13

Luqman, M., Faheem, M., Ramay, W. Y., Saeed, M. K., and Ahmad, M. B. (2024). Utilizing Ensemble Learning for Detecting Multi-Modal Fake News. IEEE Access, 12, 15037–15049. DOI: 10.1109/ACCESS.2024.3357661

Lyu, S., and Liu, J. (2020). Hybrid Framework of Convolution and Recurrent Neural Networks for Text Classification. 2020 IEEE International Conference on Knowledge Graph (ICKG), 313–320.

Mahid, Z. I., Manickam, S., and Karuppayah, S. (2018). Fake News on Social Media: Brief Review on Detection Techniques. DOI: 10.1109/ICACCAF.2018.8776689

Maree, M., Eleyat, M., and Mesqali, E. (2024). Optimizing Machine Learning-Based Sentiment Analysis Accuracy in Bilingual Sentences via Preprocessing Techniques. International Arab Journal of Information Technology. DOI: 10.34028/iajit/21/2/8

Medeiros, F. D. C., and Braga, R. (2020). Fake News Detection in Social Media: A Systematic Review. DOI: 10.1145/3411564.3411648

Mohr, F., and Rijn, J. N. (2021). Fast and Informative Model Selection Using Learning Curve Cross-Validation. IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2023.3251957

Mostafa, M. A., Almogren, A. S., and Al-Qurishi, M. (2024). Modality deep-learning frameworks for fake news detection. DOI: 10.1145/3700748

Mridha, M., Keya, A. J., and Hamid, M. A. (2021). A Comprehensive Review on Fake News Detection With Deep Learning. DOI: 10.1109/ACCESS.2021.3129329

Mushtaq, A., Iqbal, M. J., Ramzan, S. (2024). Fake News Prediction and Analysis in LIAR Dataset Using Advanced Machine Learning Techniques. Bulletin of Business and Economics (BBE). DOI: 10.61506/01.00255

Paliwal, M. S., Gupta, D., Palaniswamy, S., and Venugopalan, M. (2023). Fake news detection using deep learning and transformer-based model. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). DOI: 10.1109/ICCCNT56998.2023.10308352

Panda, R., & Kumari, S. (2022). Analysis of Deep Ensemble Transformer Model for Fake News Detection. 2022 IEEE International Conference on Data Science and Information System (ICDSIS), 1–5. DOI: 10.1109/ICDSIS55133.2022.9915941

Pandit, P. (2024). Fake News Detector. International Journal of Scientific Research in Engineering and Management. DOI:10.55041/IJSREM33362

Preston, S., Anderson, A., and Robertson, D. J. (2021). Detecting fake news on Facebook: The role of emotional intelligence. DOI: 10.1371/journal.pone.0246757

Proietti, M., Lebani, G. E., and Lenci, A. (2022). Does BERT recognize an agent? Modeling Dowty’s Proto-Roles with Contextual Embeddings. ArXiv. DOI: 10.48550/arXiv.2205.03461.

Ramya, S., and Eswari, R. (2021). Attention-Based Deep Learning Models for Detection of Fake News in Social Networks. International Journal of Cognitive Informatics and Natural Intelligence, 15, 1–25. DOI: 10.4018/IJCINI.295809

Ruchansky, N., Seo, S., and Liu, Y. (2017). CSI: A Hybrid Deep Model for Fake News Detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 797–806). ACM. DOI: 10.1145/3132847.3132877

Saini, K., and Jain, R. (2023). A hybrid LSTM-BERT and Glove-based deep learning approach for the detection of fake news. 2023 3rd International Conference on Smart Data Intelligence (ICSMDI). DOI: 10.1109/ICSMDI57622.2023.00077

Samadi, M., Mousavian, M., and Momtazi, S. (2021). Deep contextualized text representation and learning for fake news detection. Information Processing & Management, 58, 102723. DOI: 10.1016/j.ipm.2021.102723

Sathish Kumar, P., Suthanthiradevi, P., Arul Stephen, C., Abishek, B. E., Sivakumar, S., & Mathiyarasu, M. (2024). Analysis and Detection of Fake News Using Machine Learning. 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). DOI: 10.1109/AIIoT58432.2024.10574761

Setiawan, R., Ponnam, V. S., Sengan, S. (2021). Certain Investigation of Fake News Detection from Facebook and Twitter Using Artificial Intelligence Approach. Wireless Personal Communications, 127, 1737–1762. DOI: 10.1007/s11277-021-08720-9

Sharma, N., and Singh, M. (2016). Modifying Naive Bayes classifier for multinomial text classification. 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 1–7. DOI: 10.1109/ICRAIE.2016.7939519

Shim, J.-S., Lee, Y., and Ahn, H. (2021). A Link2Vec-Based Fake News Detection Model Using Web Search Results. Expert Systems with Applications, 184, 115491. DOI: 10.1016/j.eswa.2021.115491

Silva, R. A., Canuto, A., Barreto, C. A. S., and Xavier-Junior, J. C. (2021). Automatic recommendation method for classifier ensemble structure using meta-learning. IEEE Access, 9, 106254–106268.

Sitaula, N., Mohan, C., Grygiel, J., Zhou, X., and Zafarani, R. (2019). Credibility-Based Fake News Detection. ArXiv. DOI: 10.1007/978-3-030-42699-6_9.

Subhash, P. M., Gupta, D., Palaniswamy, S., and Venugopalan, M. (2023). Fake news detection using deep learning and transformer-based models. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT).DOI: 10.1109/ICCCNT56998.2023.10308352

Sun, C., Qiu, X., Xu, Y., and Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Advances in Neural Information Processing Systems. DOI: 10.1007/978-3-030-32381-3_16

Tavhare, A., Choudhary, S., Borle, K., Barve, S., and Chikmurge, D. (2023). Fake News Detection Using Ensemble Approach. 2023 IEEE Fifth International Conference on Advances in Electronics, Computers and Communications (ICAECC), 01–06.

Tenney, I., Das, D., and Pavlick, E. (2019). BERT Rediscovers the Classical NLP Pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4593–4601). DOI: 10.18653/v1/P19-1452

Thompson, R. C., Joseph, S., and Adeliyi, T. T. (2022). A Systematic Literature Review and Meta-Analysis of Online Fake News Detection. DOI: 10.3390/info13110527

Varma, R., Verma, Y., and Vijayvargiya, P. (2021). A systematic survey on fake news detection during the COVID-19 pandemic. DOI: 10.1108/IJICC-04-2021-0069

Venkatachalam, K., Al-Onazi, B. B., Simi?, V., Tirkolaee, E. B., & Jana, C. (2023). DeepFND: An Ensemble-Based Deep Learning Approach for Fake News Detection. PeerJ Computer Science. DOI: 10.7717/peerj-cs.1666.

Villela, H. F., Corrêa, F., and Ribeiro, J. S. A. N. (2023). Fake news detection: A review of machine learning techniques. DOI: 10.5753/jis.2023.3020

Vosoughi, S., Roy, D., and Aral, S. (2018). The Spread of True and False News Online. Science, 359(6380), 1146–1151. DOI: 10.1126/science.aap9559.

Wang, W. Y. (2017). "Liar, liar pants on fire": A new benchmark dataset for fake news detection. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 422–426. DOI: 10.18653/v1/P17-2067.

Wu, L., & Rao, Y. (2020). Adaptive Interaction Fusion Networks for Fake News Detection. ArXiv. DOI: 10.3233/FAIA200348

Xu, C., & Kechadi, M.-T. (2024). An Enhanced Fake News Detection System With Fuzzy Deep Learning. IEEE Access, 12, 88006–88021. DOI: 10.1109/ACCESS.2024.3418340.