nach oben

Erschienen in:

2025 | OriginalPaper | Buchkapitel

Improving the Robustness of Rumor Detection Models with Metadata-Augmented Evasive Rumor Datasets

verfasst von : Larry Huynh, Andrew Gansemer, Hyoungshick Kim, Jin B. Hong

Erschienen in: Web Information Systems Engineering – WISE 2024

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Rumors on social media can cause serious harm. Advances in NLP enable deceptive rumors resembling real posts, necessitating more robust detection. One approach collects and augments a dataset with adversarial rumors meant to evade models. Understanding evasive rumors and adding them to a dataset improves model robustness. We demonstrate effective data augmentation that significantly improves detection models. State-of-the-art accuracy drops by up to 29.5% against evasive rumors, while our augmentation raises it by up to 14.62%. Results highlight data augmentation’s importance for robust detection models countering evasion. Our evaluation shows the value of augmentation for developing models robust against adversarial attacks.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel NLWM: A Robust, Efficient and High-Quality Watermark for Large Language Models

Nächstes Kapitel Rumor Alteration for Improving Rumor Generation

https://developer.twitter.com/en.

https://github.com/serenaklm/rumor_detection.

https://github.com/zperfet/PathFakeGit.

https://colab.research.google.com/.

PPA does not use the TD feature and therefore no results are available for the test dataset variations with TD.

Achiam, J., et al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

Chen, X., Zhu, D., Lin, D., Cao, D.: Rumor knowledge embedding based data augmentation for imbalanced rumor detection. Inf. Sci. 580, 352–370 (2021)MathSciNetCrossRef

Choi, E.C., Ferrara, E.: Automated claim matching with large language models: empowering fact-checkers in the fight against misinformation. In: Companion Proceedings of the ACM on Web Conference 2024, pp. 1441–1449 (2024)

Guo, Z., Yu, K., Jolfaei, A., Bashir, A.K., Almagrabi, A.O., Kumar, N.: Fuzzy detection system for rumors through explainable adaptive learning. IEEE Trans. Fuzzy Syst. 29(12), 3650–3664 (2021)CrossRef

Huang, Q., Yu, J., Wu, J., Wang, B.: Heterogeneous graph attention networks for early detection of rumors on twitter. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)

Huang, Y., Sun, L.: Fakegpt: fake news generation, explanation and detection of large language models (2024). https://arxiv.org/abs/2310.05046

Huynh, L., Nguyen, T., Goh, J., Kim, H., Hong, J.B.: ARGH! automated rumor generation hub. In: Proceedings of the 30th ACM International Conference on Information Knowledge Management, CIKM 2021, pp. 3847–3856. Association for Computing Machinery, New York (2021)

Jacob, P., Uitdenbogerd, A.L.: Readability of twitter tweets for second language learners. In: Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association, pp. 19–27 (2019)

Khoo, L.M.S., Chieu, H.L., Qian, Z., Jiang, J.: Interpretable rumor detection in microblogs by attending to user interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence, no. 05, pp. 8783–8790 (2020)

10.

Kochkina, E., Liakata, M., Zubiaga, A.: All-in-one: multi-task learning for rumour verification. arXiv preprint arXiv:1806.03713 (2018)

11.

Le, T., Wang, S., Lee, D.: Malcom: generating malicious comments to attack neural fake news detection models. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 282–291. IEEE (2020)

12.

Lin, H., Ma, J., Chen, L., Yang, Z., Cheng, M., Chen, G.: Detect rumors in microblog posts for low-resource domains via adversarial contrastive learning. arXiv preprint arXiv:2204.08143 (2022)

13.

Liu, X., Nourbakhsh, A., Li, Q., Fang, R., Shah, S.: Real-time rumor debunking on twitter. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1867–1870 (2015)

14.

Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks (2016)

15.

Nan, Q., Cao, J., Zhu, Y., Wang, Y., Li, J.: Mdfend: multi-domain fake news detection. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3343–3347 (2021)

16.

Pattanaik, B., Mandal, S., Tripathy, R.M.: A survey on rumor detection and prevention in social media using deep learning. Knowl. Inf. Syst. 1–42 (2023)

17.

Qi, P., Yan, Z., Hsu, W., Lee, M.L.: Sniffer: multimodal large language model for explainable out-of-context misinformation detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13052–13062 (2024)

18.

Shu, K., Li, Y., Ding, K., Liu, H.: Fact-enhanced synthetic news generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 13825–13833 (2021)

19.

Singh, P., Srivastava, R., Rana, K., Kumar, V.: Semi-FND: stacked ensemble based multimodal inferencing framework for faster fake news detection. Expert Syst. Appl. 215, 119302 (2023)CrossRef

20.

Solaiman, I., et al.: Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203 (2019)

21.

Song, C., Shu, K., Wu, B.: Temporally evolving graph neural network for fake news detection. Inf. Process. Manag. 58(6), 102712 (2021)CrossRef

22.

Song, Y.Z., Chen, Y.S., Chang, Y.T., Weng, S.Y., Shuai, H.H.: Adversary-aware rumor detection. In: ACL-IJCNLP: Findings (2021)

23.

Taylor, J.: 5G fires: Australian mobile companies work with police to prevent arson attacks (2020). https://www.theguardian.com/technology/2020/may/27/5g-fires-australian-mobile-companies-work-with-police-to-prevent-arson-attacks

24.

Wu, L., Morstatter, F., Carley, K.M., Liu, H.: Misinformation in social media: definition, manipulation, and detection. ACM SIGKDD Explorations Newsl. 21(2), 80–90 (2019)CrossRef

25.

Yang, F., Li, S.: Robust rumor detection based on multi-defense model ensemble. Appl. Artif. Intell. 37(1), 2151174 (2023)CrossRef

26.

Yang, X., Lyu, Y., Tian, T., Liu, Y., Liu, Y., Zhang, X.: Rumor detection on social media with graph structured adversarial learning. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 1417–1423 (2021)

27.

Zhang, P., Ran, H., Jia, C., Li, X., Han, X.: A lightweight propagation path aggregating network with neural topic model for rumor detection. Neurocomputing 458, 468–477 (2021)CrossRef

28.

Zhu, Y., et al.: Memory-guided multi-view multi-domain fake news detection. IEEE Trans. Knowl. Data Eng. (2022)

29.

Zubiaga, A., Liakata, M., Procter, R.: Learning reporting dynamics during breaking news for rumour detection in social media. arXiv preprint arXiv:1610.07363 (2016)

Titel: Improving the Robustness of Rumor Detection Models with Metadata-Augmented Evasive Rumor Datasets
verfasst von: Larry Huynh
Andrew Gansemer
Hyoungshick Kim
Jin B. Hong
Verlag: Springer Nature Singapore
Buch: Web Information Systems Engineering – WISE 2024
Print ISBN: 978-981-9605-75-0

Electronic ISBN: 978-981-9605-76-7

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-981-96-0576-7_25

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"