References
- A. Koirala, “COVID-19 fake news dataset”, Mendeley Data, V1, Feb. 2021. https://doi.org/10.17632/zwfdmp5syg.1
- 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
- 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
- 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
- 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
- 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).
- 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).
- 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).
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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