Document Type : Methodologies
Authors
Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran.
Abstract
The widespread use of web-based forums and social media has led to an increase in news consumption. To mitigate the impact of misinformation on users' health-related decisions, it is crucial to develop machine learning models that can automatically detect and combat fake news. In this paper, we propose a novel multilingual model with dynamic transformer model called Hybrid CapsNet for Covid-19 fake news detection in English and Persian languages. Our model incorporates two dynamic pre-trained representation models that incrementally uptrain and update the word embeddings in the training phase., dynamic RoBERTa for English and dynamic ParsBERT for Persian, and two parallel classifiers with new loss function namely margin loss. By utilizing dynamic transformer and both Deep Convolutional Neural Networks (DCNN) and Capsule Neural Networks (CapsNet), we achieve better performance than state-of-the-art baselines. To evaluate the proposed model, we use two recent Covid-19 datasets in English and Persian. Our results, in terms of F1-score, demonstrate the effectiveness of the Hybrid CapsNet model. Our model outperforms existing baselines, suggesting that it can be an effective tool for detecting and combating fake news related to Covid-19 in multiple languages. Overall, our study highlights the importance of developing effective machine learning models for combating misinformation during critical events such as the Covid-19 pandemic. The proposed model has the potential to be applied to other languages and domains and can be a valuable tool for protecting public health and safety.
Keywords
- Fake News Detection
- Covid-19
- dynamic transformer
- Deep Convolutional Neural Networks
- Capsule Neural Networks
Main Subjects
[1] X. Zhang and A. A. Ghorbani, "An overview of online fake news: Characterization, detection, and discussion," Inf. Process. Manag., vol. 57, p. 102025, 2019.
[2] K. Ma, C. Tang, W. Zhang, B. Cui, K. Ji, Z. Chen, and A. Abraham, "DC-CNN: Dual-channel convolutional neural networks with attention-pooling for fake news detection," Appl. Intell., pp. 1–16, 2022.
[3] A. Bondielli and F. Marcelloni, "A survey on fake news and rumour detection techniques," Inf. Sci., vol. 497, pp. 38–55, 2019.
[4] X. Zhou and R. Zafarani, "Fake news: A survey of research, detection methods, and opportunities," arXiv:1812.00315, 2018.
[5] A. Bovet and H. A. Makse, "Influence of fake news in Twitter during the 2016 US presidential election," Nat. Commun., vol. 10, no. 1, pp. 1–14, 2019.
[6] N. Grinberg, K. Joseph, L. Friedland, B. Swire-Thompson, and D. Lazer, "Fake news on Twitter during the 2016 US presidential election," Science, vol. 363, no. 6425, pp. 374–378, 2019.
[7] J. Kim, J. Aum, S. Lee, Y. Jang, E. Park, and D. Choi, "FibVid: Comprehensive fake news diffusion dataset during the COVID-19 period," Telemat. Inform., vol. 64, p. 101688, 2021.
[8] G. Shrivastava, P. Kumar, R. P. Ojha, P. K. Srivastava, S. Mohan, and G. Srivastava, "Defensive modeling of fake news through online social networks," IEEE Trans. Comput. Soc. Syst., vol. 7, no. 5, pp. 1159–1167, 2020.
[9] A. Depoux, S. Martin, E. Karafillakis, R. Preet, A. Wilder-Smith, and H. Larson, "The pandemic of social media panic travels faster than the COVID-19 outbreak," J. Travel Med., vol. 27, no. 3, 2020.
[10] L. Li, Q. Zhang, X. Wang, J. Zhang, T. Wang, T.-L. Gao, et al., "Characterizing the propagation of situational information in social media during COVID-19 epidemic: A case study on Weibo," IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 556–562, 2020.
[11] O. D. Apuke and B. Omar, "Fake news and COVID-19: Modelling the predictors of fake news sharing among social media users," Telemat. Inform., vol. 56, p. 101475, 2021.
[12] G. Pennycook, J. McPhetres, Y. Zhang, J. G. Lu, and D. G. Rand, "Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention," Psychol. Sci., vol. 31, no. 7, pp. 770–780, 2020.
[13] CNN News, "Nigeria records chloroquine poisoning after Trump endorses it for coronavirus treatment," 2020. [Online]. Available: http://ediioncnn.com2020/03/23/africa/chloroquine-tum-nigeria-inl/ndex.html. [Accessed: May 18, 2023].
[14] N. Karimi and J. Gambrell, "Hundreds die of poisoning in Iran as fake news suggests methanol cure for virus," Times of Israel, 2020.
[15] R. Bal, S. Sinha, S. Dutta, R. Joshi, S. Ghosh, and R. Dutt, "Analysing the extent of misinformation in cancer related tweets," in Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 924–928, 2020.
[16] S. A. Memon and K. M. Carley, "Characterizing COVID-19 misinformation communities using a novel Twitter dataset," CEUR Workshop Proceedings, vol. 2699, 2020.
[17] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, et al., "RoBERTa: A robustly optimized BERT pretraining approach," arXiv:1907.11692, 2019.
[18] U. Naseem, I. Razzak, M. Khushi, P. W. Eklund, and J. Kim, "COVIDSenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis," IEEE Trans. Comput. Soc. Syst., vol. 8, no. 4, pp. 1003–1015, 2021.
[19] P. Gupta, S. Kumar, R. R. Suman, and V. Kumar, "Sentiment analysis of lockdown in India during COVID-19: A case study on Twitter," IEEE Trans. Comput. Soc. Syst., vol. 8, no. 4, pp. 992–1002, 2020.
[20] Y. Li, Q. Pan, T. Yang, S. Wang, J. Tang, and E. Cambria, "Learning word representations for sentiment analysis," Cogn. Comput., vol. 9, no. 6, pp. 843–851, 2017.
[21] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv:1301.3781, 2013.
[22] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," Trans. Assoc. Comput. Linguist., vol. 5, pp. 135–146, 2017.
[23] J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global vectors for word representation," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543, 2014.
[24] A. Khikmatullaev, J. Lehmann, and K. Singh, "Capsule neural networks for text classification," Ph.D. dissertation, Apr. 2019.
[25] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "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, 2019.
[26] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need," in Advances in Neural Information Processing Systems, pp. 5998–6008, 2017.
[27] M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, "ParsBERT: Transformer-based model for Persian language understanding," Neural Process. Lett., vol. 53, pp. 3831–3847, 2021.
[28] J. A. Nasir, O. S. Khan, and I. Varlamis, "Fake news detection: A hybrid CNN-RNN based deep learning approach," Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100007, 2021.
[29] M. H. Goldani, R. Safabakhsh, and S. Momtazi, "Convolutional neural network with margin loss for fake news detection," Inf. Process. Manag., vol. 58, no. 1, p. 102418, 2021.
[30] K. L. Tan, C. P. Lee, and K. M. Lim, "FN-Net: A deep convolutional neural network for fake news detection," in 2021 9th International Conference on Information and Communication Technology (ICOICT), pp. 331–336, 2021.
[31] R. K. Kaliyar, A. Goswami, P. Narang, and S. Sinha, "FNDNet–A deep convolutional neural network for fake news detection," Cogn. Syst. Res., vol. 61, pp. 32–44, 2020.
[32] A. Wani, I. Joshi, S. Khandve, V. Wagh, and R. Joshi, "Evaluating deep learning approaches for COVID19 fake news detection," in International Workshop on Combating Online Hostile Posts in Regional Languages during Emerging Situations, pp. 153–163, 2021.
[33] Q. Jiang, L. Chen, R. Xu, X. Ao, and M. Yang, "A challenge dataset and effective models for aspect-based sentiment analysis," in *Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*, pp. 6280–6285, 2019.
[34] M. H. Goldani, S. Momtazi, and R. Safabakhsh, "Detecting fake news with capsule neural networks," Appl. Soft Comput., vol. 101, p. 106991, 2021.
[35] B. Palani, S. Elango, and V. Viswanathan K, "CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT," Multimed. Tools Appl., pp. 1–34, 2021.
[36] S. Sridhar and S. Sanagavarapu, "Fake news detection and analysis using multitask learning with BiLSTM CapsNet model," in 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 905–911, 2021.
[37] M. Yang, W. Zhao, J. Ye, Z. Lei, Z. Zhao, and S. Zhang, "Investigating capsule networks with dynamic routing for text classification," in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3110–3119, 2018.
[38] P. Patwa, S. Sharma, S. Pykl, V. Guptha, G. Kumari, M. S. Akhtar, et al., "Fighting an infodemic: COVID-19 fake news dataset," in International Workshop on Combating Online Hostile Posts in Regional Languages during Emerging Situations, pp. 21–29, 2021.
[39] S. Shifath, M. F. Khan, M. Islam, et al., "A transformer based approach for fighting COVID-19 fake news," arXiv:2101.12027, 2021.
[40] M. Samadi, M. Mousavian, and S. Momtazi, "Deep contextualized text representation and learning for fake news detection," Inf. Process. Manag., vol. 58, no. 6, p. 102723, 2021.
[41] R. Vijjali, P. Potluri, S. Kumar, and S. Teki, "Two stage transformer model for COVID-19 fake news detection and fact checking," in Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pp. 1–10, 2020.
[42] B. Koloski, T. Stepišnik-Perdih, S. Pollak, and B. Škrlj, "Identification of COVID-19 related fake news via neural stacking," in International Workshop on Combating Online Hostile Posts in Regional Languages during Emerging Situations, pp. 177–188, 2021.
[43] M. Ghayoomi and M. Mousavian, "Deep transfer learning for COVID-19 fake news detection in Persian," Expert Syst., vol. 39, no. 8, p. e13008, 2022.
[44] D. K. Vishwakarma, P. Meel, A. Yadav, and K. Singh, "A framework of fake news detection on web platform using ConvNet," Soc. Netw. Anal. Min., vol. 13, no. 1, p. 24, 2023.
[45] D. Mehta, A. Dwivedi, A. Patra, and M. Anand Kumar, "A transformer-based architecture for fake news classification," Soc. Netw. Anal. Min., vol. 11, pp. 1–12, 2021.
[46] A. Choudhary and A. Arora, "Linguistic feature based learning model for fake news detection and classification," Expert Syst. Appl., vol. 169, p. 114171, 2021.
[47] C. Blackledge and A. Atapour-Abarghouei, "Transforming fake news: Robust generalisable news classification using transformers," arXiv:2109.09796, 2021.
[48] K. Ma, C. Tang, W. Zhang, B. Cui, K. Ji, Z. Chen, and A. Abraham, "DC-CNN: Dual-channel convolutional neural networks with attention-pooling for fake news detection," Appl. Intell., vol. 53, no. 7, pp. 8354–8369, 2023.
[49] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, et al., "Language models are few-shot learners," Adv. Neural Inf. Process. Syst., vol. 33, pp. 1877–1901, 2020.
[50] C. Chen and K. Shu, "Can LLM-generated misinformation be detected?" in NeurIPS 2023 Workshop on Regulatable ML, 2023. [Online]. Available: http://openreview.net/foru?id=Mzu0NIMvvh. [Accessed: May 18, 2023].
[51] C. Chen and K. Shu, "Combating misinformation in the age of LLMs: Opportunities and challenges," AI Mag., vol. 45, no. 3, pp. 354–368, 2024.
[52] H. Wan, S. Feng, Z. Tan, H. Wang, Y. Tsvetkov, and M. Luo, "DELL: Generating reactions and explanations for LLM-based misinformation detection," in Findings of the Association for Computational Linguistics: ACL 2024, pp. 2637–2667, 2024.
[53] M. Garry, W. M. Chan, J. Foster, and L. A. Henkel, "Large language models (LLMs) and the institutionalization of misinformation," Trends Cogn. Sci., 2024.
[54] A. Liu, Q. Sheng, and X. Hu, "Preventing and detecting misinformation generated by large language models," in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3001–3004, 2024.
[55] Y. Kim, "Convolutional neural networks for sentence classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751, 2014.
[56] B. Zhong, X. Xing, P. Love, X. Wang, and H. Luo, "Convolutional neural network: Deep learning-based classification of building quality problems," Adv. Eng. Inform., vol. 40, pp. 46–57, 2019.
[57] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," in International Conference on Learning Representations, 2015.
[58] G. Pennycook, J. McPhetres, Y. Zhang, and D. G. Rand, "The COVID-19 misinformation pandemic: A call for action," Nat. Hum. Behav., vol. 4, no. 5, pp. 460–461, 2020.
[59] S. Shahsavari, P. Holur, T. Wang, and T. R. Tangherlini, "Misinformation and disinformation in Persian-language social media: A case study of COVID-19," J. Med. Internet Res., vol. 22, no. 12, p. e22501, 2020.
[60] J. S. Brennen, F. M. Simon, P. N. Howard, and R. K. Nielsen, "Types, sources, and claims of COVID-19 misinformation," Reuters Institute for the Study of Journalism, 2020.
[61] F. Hafezi and M. Khodabakhsh, "Coronavirus incidence rate estimation from social media data in Iran," J. Artif. Intell. Data Min., vol. 11, no. 2, pp. 315–329, 2023.
[62] X. Zhang and A. A. Ghorbani, "CrossFake: A cross-lingual framework for detecting fake news in low-resource languages," in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1234–1245, 2021.
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