An Improved FakeBERT for Fake News Detection

[1] A. Koirala, “COVID-19 fake news dataset”, Mendeley Data, V1, Feb. 2021. https://doi.org/10.17632/zwfdmp5syg.1 Search in Google Scholar

[2] S. Singhania, N. Fernandez, and S. Rao, “3HAN: A deep neural network for fake news detection,” in Proc. of the International conference on neural information processing, Guangzhou, China, Oct. 2017, pp. 572–581. https://doi.org/10.1007/978-3-319-70096-0_59 Search in Google Scholar

[3] V.V. Hirlekar and A. Kumar, “Natural language processing based online fake news detection challenges – a detailed review,” in Proc. ICCES, Coimbatore, India, Jun. 2020, pp. 748–754. https://doi.org/10.1109/ICCES48766.2020.9137915 Search in Google Scholar

[4] R. K. Kaliyar, A. Goswami, and P. Narang, “FakeBERT: fake news detection in social media with a BERT-based deep learning approach,” Multimedia Tools and Applications, vol. 80, pp. 11765–11788, Jan. 2021. https://doi.org/10.1007/s11042-020-10183-2 Search in Google Scholar

[5] C. Lee, Z. Gao, and C. Tsai, “BERT-based stock market sentiment analysis,” in IEEE International Conference on Consumer Electronics -Taiwan (ICCE-Taiwan), Taoyuan, Taiwan, Sep. 2020, pp. 1–2. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258102 Search in Google Scholar

[6] R. Heilweil, “Coronavirus scammers are flooding social media with fake cures and tests,” Apr. 2020. [Online]. Available: https://www.vox.com/recode/2020/4/17/21221692/digital-black-market-covid-19-coronavirus-instagram-twitter-ebay (Accessed: December 20, 2021). Search in Google Scholar

[7] S. Reilly, J. Palamdino, J. Lambert, and M. Stiles, “Fake vaccine cards are everywhere. It’s a public health nightmare,” Grid News, 2022. [Online]. Available: https://www.grid.news/story/science/2022/01/25/fake-vaccine-cards-are-everywhere-its-a-public-health-nightmare/ (Accessed: March 20, 2022). Search in Google Scholar

[8] Woodward and Alex, “‘Fake news’: A guide to Trump’s favorite phrase – and the dangers it obscures,” Independent Digital News and Media, 2020. [Online]. Available: https://www.independent.co.uk/news/world/americas/us-election/trump-fake-news-counter-history-b732873.html (Accessed: December 20, 2021). Search in Google Scholar

[9] F. Barcala, J. Vilares, M. Alonso, J. Grana, and M. Vilares, “Tokenization and proper noun recognition for information retrieval,” in Proc.of 13th International Workshop on Database and Expert Systems Applications, Aix-en-Provence, France, 2002, pp. 246–250. Search in Google Scholar

[10] D. J. Ladani and N. P. Desai, “Stopword identification and removal techniques on TC and IR applications: A survey,” in Proc. of the 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, Mar. 2020, pp. 466–472. https://doi.org/10.1109/ICACCS48705.2020.9074166 Search in Google Scholar

[11] P. Han, S. Shen, D. Wang, and Y. Liu, “The influence of word normalization in English document clustering,” in Proc. of IEEE International Conference on Computer Science and Automation Engineering (CSAE), Zhangjiajie, China, May 2012, pp. 116–120. https://doi.org/10.1109/CSAE.2012.6272740 Search in Google Scholar

[12] S. A. Salloum, R. Khan, and K. Shaalan, “A survey of semantic analysis approaches.” in Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), Advances in Intelligent Systems and Computing, vol. 1153, A.E. Hassanien, A. Azar, T. Gaber, D. Oliva, and F. Tolba, Eds. Springer, Cham., Mar. 2020. https://doi.org/10.1007/978-3-030-44289-7_6 Search in Google Scholar

[13] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding, ” Computation and Language, pp. 1–16, 2018. [Online]. Available: https://aclanthology.org/N19-1423.pdf Search in Google Scholar

[14] P. A. Vikhar, “Evolutionary algorithms: A critical review and its future prospects,” in Proc. of International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India, Dec. 2016, pp. 261–265. https://doi.org/10.1109/ICGTSPICC.2016.7955308 Search in Google Scholar

[15] Y. Yusoff, M. Salihin, N. Azlan, and M. Zain, “Overview of NSGA-II for optimizing machining process parameters, ” Procedia Engineering, vol. 15, pp. 3978–3983, 2011. https://doi.org/10.1016/j.proeng.2011.08.745 Search in Google Scholar

[16] R. A. Monteiro, R. L. S. Santos, T. A. S. Pardo, T. A. de Almeida, E.E.S. Ruiz, and O. A. Vale, “Contributions to the study of fake news in portuguese: new corpus and automatic detection results,” in Proc. of International Conference on Computational Processing of the Portuguese Language, Canela, Brazil, 2018, pp. 324–334. https://doi.org/10.1007/978-3-319-99722-3_33 Search in Google Scholar

[17] C. Lui et al., “A two-stage model based on BERT for short fake news detection,” in Proc. of International Conference on Knowledge Science, Engineering and Management, Athens, Greece, Aug. 2019, pp. 172–183. https://doi.org/10.1007/978-3-030-29563-9_17 Search in Google Scholar

[18] H. Jwa, D. Oh, K. Park, J. M. Kang, and H. Lim, “exBAKE: automatic fake news detection model based on bidirectional encoder representations from transformers (BERT),” Applied Sciences., vol. 9, no. 19, Sep. 2019, Art. no. 4062. https://doi.org/10.3390/app9194062 Search in Google Scholar

[19] A. Jain, A. Shakya, H. Khatter, and A. K. Gupta, “A smart system for fake news detection using machine learning,” in Proc. of International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, Sep. 2019, pp. 1–6. https://doi.org/10.1109/ICICT46931.2019.8977659 Search in Google Scholar

[20] T. Zhang et al., “BDANN: BERT-based domain adaptation neural network for multi-modal fake news detection,” in Proc. of International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, Jul. 2020, pp. 1–8. https://doi.org/10.1109/IJCNN48605.2020.9206973 Search in Google Scholar

[21] R. K. Kaliyar, A. Goswami, P. Narang, and S. Sinha, “FNDNet – A deep convolutional neural network for fake news detection,” Cognitive Systems Research, vol. 61, pp. 32-44, Jun. 2020. https://doi.org/10.1016/j.cogsys.2019.12.005 Search in Google Scholar

[22] M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G. S. Choi, and B.W. On, “Fake news stance detection using deep learning architecture (CNNLSTM),” IEEE Access, vol. 8, pp. 156695–156706, Aug. 2020. https://doi.org/10.1109/ACCESS.2020.3019735 Search in Google Scholar

[23] J. A. Nasir, O. S. Khan, and I. Varlamis, “Fake news detection: a hybrid CNN-RNN based deep learning approach,” International Journal of Information Management Data Insights, vol. 1, no. 1, Apr. 2021, Art. no. 100007. https://doi.org/10.1016/j.jjimei.2020.100007 Search in Google Scholar

[24] B. Al-Ahmad, M. A. Al-Zoubi, R. A. Kurma, and I. Aljarah, “An evolutionary fake news detection method for COVID-19 pandemic information,” Asymmetry, vol. 13, no. 6, Jun. 2021, Art. no. 1091. https://doi.org/10.3390/sym13061091 Search in Google Scholar

[25] S. Liu, H. Tao, and S. Feng, “Text classification research based on BERT model and Bayesian network,” in Proc. of Chinese Automation Congress (CAC), Hangzhou, China, Nov. 2019, pp. 5842–5846. https://doi.org/10.1109/CAC48633.2019.8996183 Search in Google Scholar

[26] M. A.Hassonaha, R. Al-Sayyeda, and A. Rodan, “An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on Twitter,” Knowledge-Based Systems, vol. 192, Mar. 2020, Art. no. 105353. https://doi.org/10.1016/j.knosys.2019.105353 Search in Google Scholar

[27] Y. Fors-Isalguez and J. Hemosillo-Valadez, “Query-oriented text summarization based on multiobjective evolutionary algorithms and word embeddings,” Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3235–3244, May 2018. https://doi.org/10.3233/JIFS-169506 Search in Google Scholar

[28] R. Alqaisi, W. Ghanem, and A. Qaroush, “Extractive multi-document arabic text summarization using evolutionary multi -objective optimization with K-medoid clustering,” IEEE Access, vol. 8, pp. 228206–228224, Dec. 2020. https://doi.org/10.1109/ACCESS.2020.3046494 Search in Google Scholar