Abstract
Widespread dissemination of misinformation over online social media has brought about negative consequences that disrupt lives on so many levels, from personal to an entire society. Inspired by ongoing occurrences, this study aims to explore an efficient method that can detect misinformation accurately since it is a key to help prevent or at least mitigate the chaos that arises due to misleading or false claims. The paper proposes a deep neural architecture that leverages the capability of hierarchical attention networks together with capsule networks to learn effective representation for the misinformation detection task. Our finding suggests that the hierarchical structure of each event as well as capsule networks are contributing factors that lead to overall performance gain. Results from extensive experiments conducted on two real-world datasets indicate that the proposed approach can accurately detect events that carry misinformation, outweighing a range of competitive baselines.



Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Availability of Data and Materials
Not applicable.
Code Availability
Not applicable.
Notes
https://www.nytimes.com/article/coronavirus-timeline.html.
References
Romer D, Jamieson KH. Conspiracy theories as barriers to controlling the spread of Covid-19 in the us. Soc Sci Med. 2020;263: 113356.
Schaeffer K. A look at the Americans who believe there is some truth to the conspiracy theory that COVID-19 was planned. https://pewrsr.ch/3f1dgPo. Accessed 2021-08-18.
WHO. Fighting misinformation in the time of COVID-19, one click at a time. https://www.who.int/news-room/feature-stories/detail/fighting-misinformation-in-the-time-of-covid-19-one-click-at-a-time. Accessed 2021-08-18.
de Oliveira DVB, Albuquerque UP. Cultural evolution and digital media: diffusion of fake news about Covid-19 on twitter. SN Comput Sci. 2021;2(6):1–12.
Castillo C, Mendoza M, Poblete B. Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web. 2011. p. 675–4.
Kwon S, Cha M, Jung K, Chen W, Wang Y. Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data Mining. IEEE; 2013. p. 1103–8.
Ma J, Gao W, Wei Z, Lu Y, Wong K-F. Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015. p. 1751–4.
Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong K-F, Cha M. Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. IJCAI’16; 2016. p. 3818–24.
Yu F, Liu Q, Wu S, Wang L, Tan T. A convolutional approach for misinformation identification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. IJCAI’17; 2017. p. 3901–7.
Ma J, Gao W, Wong K-F. Detect rumors on twitter by promoting information campaigns with generative adversarial learning. In: The World Wide Web Conference. WWW ’19. New York: Association for Computing Machinery; 2019. p. 3049–55. https://doi.org/10.1145/3308558.3313741.
Zhou K, Shu C, Li B, Lau JH. Early rumour detection. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019. p. 1614–23.
Yuan C, Qian W, Ma Q, Zhou W, Hu S. SRLF: a stance-aware reinforcement learning framework for content-based rumor detection on social media. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE; 2021. p. 1– 8.
Kaplan AM, Haenlein M. The early bird catches the news: nine things you should know about micro-blogging. Bus Horiz. 2011;54(2):105–13.
Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016. p. 1480–9.
Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D, et al. The science of fake news. Science. 2018;359(6380):1094–6.
Zhao Z, Resnick P, Mei Q. Enquiring minds: Early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web. 2015. p. 1395–1405.
Qiao Y, Wiechmann D, Kerz E. A language-based approach to fake news detection through interpretable features and BRNN. In: Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM). 2020. p. 14–31.
Chen T, Li X, Yin H, Zhang J. Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer; 2018. 40–52.
Meesad P. Thai fake news detection based on information retrieval, natural language processing and machine learning. SN Comput Sci. 2021;2(6):1–17.
Agarwal A, Mittal M, Pathak A, Goyal LM. Fake news detection using a blend of neural networks: an application of deep learning. SN Comput Sci. 2020;1(3):1–9.
Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, et al. Google’s neural machine translation system: bridging the gap between human and machine translation. 2016. arXiv preprint arXiv:1609.08144.
Lee K, He L, Lewis M, Zettlemoyer L. End-to-end neural coreference resolution. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics; 2017. p. 188–97. https://doi.org/10.18653/v1/D17-1018. https://aclanthology.org/D17-1018.
Yu F, Liu Q, Wu S, Wang L, Tan T. Attention-based convolutional approach for misinformation identification from massive and noisy microblog posts. Comput Secur. 2019;83:106–21.
Tarnpradab S, Hua KA. Attention based neural architecture for rumor detection with author context awareness. In: 2018 Thirteenth International Conference on Digital Information Management (ICDIM). IEEE; 2018. p. 82–7.
Tarnpradab S, Liu F, Hua KA. Toward extractive summarization of online forum discussions via hierarchical attention networks. In: The Thirtieth International Flairs Conference, 2017.
Tarnpradab S, Jafariakinabad F, Hua KA. Improving online forums summarization via hierarchical unified deep neural network. 2021. arXiv preprint arXiv:2103.13587
Guo H, Cao J, Zhang Y, Guo J, Li J. Rumor detection with hierarchical social attention network. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018. p. 943–51.
Khoo LMS, Chieu HL, Qian Z, Jiang J. Interpretable rumor detection in microblogs by attending to user interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34. 2020. p. 8783–90.
Ruchansky N, Seo S, Liu Y. CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017. p. 797–806.
Li Q, Zhang Q, Si L. Rumor detection by exploiting user credibility information, attention and multi-task learning. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. p. 1173–9.
Ma J, Gao W, Wong K-F. Detect rumors in microblog posts using propagation structure via kernel learning. Copenhagen: Association for Computational Linguistics; 2017.
Kwon S, Cha M, Jung K. Rumor detection over varying time windows. PLoS ONE. 2017;12(1):0168344.
Liu Y, Jin X, Shen H, Cheng X. Do rumors diffuse differently from non-rumors? A systematically empirical analysis in Sina Weibo for rumor identification. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer; 2017. p. 407–20.
Jin Z, Cao J, Jiang Y-G, Zhang Y. News credibility evaluation on microblog with a hierarchical propagation model. In: 2014 IEEE International Conference on Data Mining. IEEE; 2014. p. 230–9.
Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders. In: International Conference on Artificial Neural Networks. Springer; 2011. p. 44–51.
Sabour S, Frosst N, Hinton GE. Dynamic routing between capsules. Adv Neural Inf Process Syst. 2017;30.
Zhao W, Ye J, Yang M, Lei Z, Zhang S, Zhao Z. Investigating capsule networks with dynamic routing for text classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics; 2018, p. 3110–9. https://doi.org/10.18653/v1/D18-1350. https://aclanthology.org/D18-1350.
Wang Z, Hu X, Ji S. iCapsNets: towards interpretable capsule networks for text classification. 2020. arXiv preprint arXiv:2006.00075.
Gangwar AK, Ravi V. A novel BGcapsule network for text classification. SN Comput Sci. 2022;3(1):1–12.
Cho S, Lebanoff L, Foroosh H, Liu F. Improving the similarity measure of determinantal point processes for extractive multi-document summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics; 2019. p. 1027–38. https://doi.org/10.18653/v1/P19-1098. https://aclanthology.org/P19-1098.
Acharya HR, Bhat AD, Avinash K, Srinath R. LegoNet-classification and extractive summarization of Indian legal judgments with capsule networks and sentence embeddings. J Intell Fuzzy Syst. 2020;39(2):2037–46.
Goldani MH, Momtazi S, Safabakhsh R. Detecting fake news with capsule neural networks. Appl Soft Comput. 2021;101: 106991. https://doi.org/10.1016/j.asoc.2020.106991.
Samadi M, Mousavian M, Momtazi S. Deep contextualized text representation and learning for fake news detection. Inf Process Manag. 2021;58(6): 102723.
Zhang X, Wu K, Chen Z, Zhang C. MalCaps: a capsule network based model for the malware classification. Processes. 2021. https://doi.org/10.3390/pr9060929.
Yin S-L, Zhang X-L, Liu S. Intrusion detection for capsule networks based on dual routing mechanism. Comput Netw. 2021;197: 108328. https://doi.org/10.1016/j.comnet.2021.108328.
Sujana Y, Li J, Kao H-Y. Rumor detection on Twitter using multiloss hierarchical BiLSTM with an attenuation factor. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. Suzhou: Association for Computational Linguistics; 2020. p. 18–26. https://aclanthology.org/2020.aacl-main.3.
Amir S, Coppersmith G, Carvalho P, Silva MJ, Wallace BC. Quantifying mental health from social media with neural user embeddings. In: Machine Learning for Healthcare Conference. PMLR; 2017. p. 306–21.
Zhang Y, Wallace B. A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Taipei: Asian Federation of Natural Language Processing; 2017. p. 253–63. https://aclanthology.org/I17-1026.
Zeiler MD. AdaDelta: an adaptive learning rate method. 2012. arXiv:1212.5701.
Duchi J, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res. 2011;12(7).
Kingma DP, Ba J. Adam: a method for stochastic optimization. CoRR. 2015. arXiv:1412.6980.
Xia R, Xuan K, Yu J. A state-independent and time-evolving network with applications to early rumor detection. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. p. 9042–51.
Bing C, Wu Y, Dong F, Xu S, Liu X, Sun S. Dual co-attention-based multi-feature fusion method for rumor detection. Information. 2022. https://doi.org/10.3390/info13010025.
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst. 2013;26.
Funding
This research received no external funding.
Author information
Authors and Affiliations
Contributions
Conceptualization, ST; methodology, ST; software, ST; validation, ST; formal analysis, ST; investigation, ST and KH; resources, ST; data curation, ST; writing—-original draft preparation, ST; writing—review and editing, ST and KH; visualization, ST; supervision, KH; project administration, KH.
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tarnpradab, S., Hua, K.A. End-to-End Deep Networks with Hierarchical Attention and Capsule Capabilities for Misinformation Detection on Microblogging Platforms. SN COMPUT. SCI. 5, 255 (2024). https://doi.org/10.1007/s42979-023-02594-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02594-3