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How to cite this paper
APA: Gonwirat, S., Choompol, A & Wichapa, N. (2022). A combined deep learning model based on the ideal distance weighting method for fake news detection. International Journal of Data and Network Science, 6(2), 347-354.
Chicago/Turabian: Gonwirat, S., Choompol, A & Wichapa, N. 2022. "A combined deep learning model based on the ideal distance weighting method for fake news detection." International Journal of Data and Network Science 6, no. 2 (2022): 347-354.
AMA: Gonwirat, S., Choompol, A & Wichapa, N. A combined deep learning model based on the ideal distance weighting method for fake news detection. International Journal of Data and Network Science. 2022;6(2):347-354.
References
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Popat, K., Mukherjee, S., Strötgen, J., & Weikum, G. (2017). Where the truth lies: Explaining the credibility of emerging claims on the web and social media. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1003–1012).
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Tan, C. J., Lim, C. P., & Cheah, Y. (2014). A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models. Neurocomputing, 125, 217–228.
Thota, A., Tilak, P., Ahluwalia, S., & Lohia, N. (2018). Fake news detection: a deep learning approach. SMU Data Science Review, 1(3), 10.
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Yan, X., He, F., Zhang, Y., & Xie, X. (2019). An optimizer ensemble algorithm and its application to image registration. Integrated Computer-Aided Engineering, 26(4), 311–327.
Zarrella, G., & Marsh, A. (2016). Mitre at semeval-2016 task 6: Transfer learning for stance detection. ArXiv Preprint ArXiv:1606.03784.
Ahmed, H., Traore, I., & Saad, S. (2018). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1), 1-15.
Aldwairi, M., & Alwahedi, A. (2018). Detecting fake news in social media networks. Procedia Computer Science, 141(1), 215–222.
Augenstein, I., Rocktäschel, T., Vlachos, A., & Bontcheva, K. (2016). Stance detection with bidirectional conditional encoding. ArXiv Preprint ArXiv:1606.05464.
Awan, M. J. (2020). Fake News Classification Bimodal using Convolutional Neural Network and Long Short-Term Memory. Article in International Journal of Emerging Technologies in Learning (IJET), 11(5), 209–212.
Borovkova, S., & Tsiamas, I. (2019). An ensemble of LSTM neural networks for high‐frequency stock market classification. Journal of Forecasting, 38(6), 600–619.
Choudhary, M., Chouhan, S. S., Pilli, E. S., & Vipparthi, S. K. (2021). BerConvoNet: A deep learning framework for fake news classification. Applied Soft Computing, 110, 107614.
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv Preprint ArXiv:1412.3555.
He, H., Gimpel, K., & Lin, J. (2015). Multi-perspective sentence similarity modeling with convolutional neural networks. In Proceedings of the 2015 conference on empirical methods in natural language processing (pp. 1576–1586).
Huang, Y.-F., & 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.
Jindal, N., & Liu, B. (2008). Opinion spam and analysis. In Proceedings of the 2008 international conference on web search and data mining (pp. 219–230).
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. ArXiv Preprint ArXiv:1412.6980.
Koirala, A. (2020). COVID-19 Fake News Classification with Deep Learning. Preprint.
Li, J., Cardie, C., & Li, S. (2013). Topicspam: a topic-model based approach for spam detection. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 217–221).
Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., & Cherry, C. (2016). Semeval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 31–41).
Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), pp.100007.
Popat, K., Mukherjee, S., Strötgen, J., & Weikum, G. (2017). Where the truth lies: Explaining the credibility of emerging claims on the web and social media. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1003–1012).
Rojarath, A., & Songpan, W. (2021). Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems. Applied Intelligence, 51, 4908–4932.
Ruder, S. (2016). An overview of gradient descent optimization algorithms. ArXiv Preprint ArXiv:1609.04747.
Stab, C., Miller, T., & Gurevych, I. (2018). Cross-topic argument mining from heterogeneous sources using attention-based neural networks. ArXiv Preprint ArXiv:1802.05758.
Tan, C. J., Lim, C. P., & Cheah, Y. (2014). A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models. Neurocomputing, 125, 217–228.
Thota, A., Tilak, P., Ahluwalia, S., & Lohia, N. (2018). Fake news detection: a deep learning approach. SMU Data Science Review, 1(3), 10.
Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S., & On, B.-W. (2020). Fake news stance detection using deep learning architecture (CNN-LSTM). IEEE Access, 8, 156695–156706.
Xia, J., Pan, S., Zhu, M., Cai, G., Yan, M., Su, Q., … Ning, G. (2019). A long short-term memory ensemble approach for improving the outcome prediction in intensive care unit. Computational and Mathematical Methods in Medicine, 2019, pp.1-10.
Yan, X., He, F., Zhang, Y., & Xie, X. (2019). An optimizer ensemble algorithm and its application to image registration. Integrated Computer-Aided Engineering, 26(4), 311–327.
Zarrella, G., & Marsh, A. (2016). Mitre at semeval-2016 task 6: Transfer learning for stance detection. ArXiv Preprint ArXiv:1606.03784.