More Web Proxy on the site http://driver.im/

research-article

Zero-shot rumor detection with propagation structure via prompt learning

AUTHORs:

Ruifang LiuAuthors Info & Claims

AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence

Article No.: 582, Pages 5213 - 5221

https://doi.org/10.1609/aaai.v37i4.25651

Published: 07 February 2023 Publication History

Abstract

The spread of rumors along with breaking events seriously hinders the truth in the era of social media. Previous studies reveal that due to the lack of annotated resources, rumors presented in minority languages are hard to be detected. Furthermore, the unforeseen breaking events not involved in yesterday's news exacerbate the scarcity of data resources. In this work, we propose a novel zero-shot framework based on prompt learning to detect rumors falling in different domains or presented in different languages. More specifically, we firstly represent rumor circulated on social media as diverse propagation threads, then design a hierarchical prompt encoding mechanism to learn language-agnostic contextual representations for both prompts and rumor data. To further enhance domain adaptation, we model the domain-invariant structural features from the propagation threads, to incorporate structural position representations of influential community response. In addition, a new virtual response augmentation method is used to improve model training. Extensive experiments conducted on three real-world datasets demonstrate that our proposed model achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.

References

[1]

Alam, F.; Shaar, S.; Dalvi, F.; Sajjad, H.; Nikolov, A.; Mubarak, H.; Da San Martino, G.; Abdelali, A.; Durrani, N.; Darwish, K.; et al. 2021. Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society. In Findings of the Association for Computational Linguistics: EMNLP 2021, 611-649.

[2]

Allport, G. W.; and Postman, L. J. 1945. Section of psychology: The basic psychology of rumor. Transactions of the New York Academy of Sciences, 8(2 Series II): 61-81.

[3]

Ba, J. L.; Kiros, J. R.; and Hinton, G. E. 2016. Layer normalization. arXiv preprint arXiv:1607.06450.

[4]

Bian, T.; Xiao, X.; Xu, T.; Zhao, P.; Huang, W.; Rong, Y.; and Huang, J. 2020. Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks. In Proceedings of the AAAI Conference on Artificial Intelligence.

[5]

Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020. Language models are few-shot learners. Advances in neural information processing systems.

[6]

Castillo, C.; Mendoza, M.; and Poblete, B. 2011. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, 675-684.

[7]

Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; and Kuksa, P. 2011. Natural language processing (almost) from scratch. Journal of machine learning research, 12(ARTICLE): 2493-2537.

[8]

De, A.; Bandyopadhyay, D.; Gain, B.; and Ekbal, A. 2021. A Transformer-Based Approach to Multilingual Fake News Detection in Low-Resource Languages. Transactions on Asian and Low-Resource Language Information Processing.

[9]

Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. 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, Volume 1 (Long and Short Papers), 4171-4186.

[10]

Du, J.; Dou, Y.; Xia, C.; Cui, L.; Ma, J.; and Philip, S. Y. 2021. Cross-lingual COVID-19 Fake News Detection. In 2021 International Conference on Data Mining Workshops (ICDMW), 859-862. IEEE.

[11]

Friggeri, A.; Adamic, L.; Eckles, D.; and Cheng, J. 2014. Rumor cascades. In Eighth international AAAI conference on weblogs and social media.

[12]

Gehring, J.; Auli, M.; Grangier, D.; Yarats, D.; and Dauphin, Y. N. 2017. Convolutional sequence to sequence learning. In International conference on machine learning. PMLR.

[13]

Guo, H.; Cao, J.; Zhang, Y.; Guo, J.; and Li, J. 2018. Rumor detection with hierarchical social attention network. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 943-951.

[14]

Hannak, A.; Margolin, D.; Keegan, B.; and Weber, I. 2014. Get Back! You Don't Know Me Like That: The Social Mediation of Fact Checking Interventions in Twitter Conversations. In ICWSM.

[15]

He, J.; Zhou, C.; Ma, X.; Berg-Kirkpatrick, T.; and Neubig, G. 2021. Towards a Unified View of Parameter-Efficient Transfer Learning. In International Conference on Learning Representations.

[16]

Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019. Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning, 2790-2799. PMLR.

[17]

Huang, L.; Ma, S.; Zhang, D.; Wei, F.; and Wang, H. 2022. Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt. arXiv preprint arXiv:2202.11451.

[18]

Jawahar, G.; Sagot, B.; and Seddah, D. 2019. What does BERT learn about the structure of language? In ACL 201957th Annual Meeting of the Association for Computational Linguistics.

[19]

Ke, L.; Chen, X.; Lu, Z.; Su, H.; and Wang, H. 2020. A novel approach for cantonese rumor detection based on deep neural network. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 1610-1615. IEEE.

[20]

Khoo, L. M. S.; Chieu, H. L.; Qian, Z.; and Jiang, J. 2020. Interpretable rumor detection in microblogs by attending to user interactions. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 8783-8790.

[21]

Kwon, S.; Cha, M.; Jung, K.; Chen, W.; and Wang, Y. 2013. Prominent features of rumor propagation in online social media. In 2013 IEEE 13th International Conference on Data Mining, 1103-1108. IEEE.

[22]

Lee, C.-H.; Cheng, H.; and Ostendorf, M. 2021. Dialogue State Tracking with a Language Model using Schema-Driven Prompting. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 4937-4949.

[23]

Lester, B.; Al-Rfou, R.; and Constant, N. 2021. The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 3045-3059.

[24]

Li, X. L.; and Liang, P. 2021. Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing.

[25]

Lin, H.; Ma, J.; Chen, L.; Yang, Z.; Cheng, M.; and Guang, C. 2022. Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning. In Findings of the Association for Computational Linguistics: NAACL 2022.

[26]

Lin, H.; Ma, J.; Cheng, M.; Yang, Z.; Chen, L.; and Chen, G. 2021a. Rumor detection on twitter with claim-guided hierarchical graph attention networks. In Proceedings of the 2021 conference on empirical methods in natural language processing (EMNLP).

[27]

Lin, H.; Yan, Y.; and Chen, G. 2021. Boosting Low-Resource Intent Detection with in-Scope Prototypical Networks. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[28]

Lin, X. V.; Mihaylov, T.; Artetxe, M.; Wang, T.; Chen, S.; Simig, D.; Ott, M.; Goyal, N.; Bhosale, S.; Du, J.; et al. 2021b. Few-shot Learning with Multilingual Language Models. arXiv preprint arXiv:2112.10668.

[29]

Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; and Neubig, G. 2021a. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586.

[30]

Liu, X.; Ji, K.; Fu, Y.; Du, Z.; Yang, Z.; and Tang, J. 2021b. P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks. arXiv preprint arXiv:2110.07602.

[31]

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

[32]

Loshchilov, I.; and Hutter, F. 2018. Decoupled Weight Decay Regularization. In International Conference on Learning Representations.

[33]

Ma, J.; Gao, W.; Mitra, P.; Kwon, S.; Jansen, B. J.; Wong, K.-F.; and Cha, M. 2016. Detecting rumors from microblogs with recurrent neural networks. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 3818-3824.

[34]

Ma, J.; Gao, W.; and Wong, K.-F. 2017. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.

[35]

Ma, J.; Gao, W.; and Wong, K.-F. 2018. Rumor Detection on Twitter with Tree-structured Recursive Neural Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.

[36]

Min, S.; Lewis, M.; Hajishirzi, H.; and Zettlemoyer, L. 2022. Noisy Channel Language Model Prompting for Few-Shot Text Classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics.

[37]

Miyato, T.; Maeda, S.-i.; Koyama, M.; and Ishii, S. 2018. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE transactions on pattern analysis and machine intelligence.

[38]

Rao, D.; Miao, X.; Jiang, Z.; and Li, R. 2021. STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media. In EMNLP.

[39]

Rozsa, A.; Rudd, E. M.; and Boult, T. E. 2016. Adversarial diversity and hard positive generation. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 25-32.

[40]

Schucher, N.; Reddy, S.; and de Vries, H. 2021. The Power of Prompt Tuning for Low-Resource Semantic Parsing. arXiv preprint arXiv:2110.08525.

[41]

Schwarz, S.; Theóphilo, A.; and Rocha, A. 2020. Emet: Embeddings from multilingual-encoder transformer for fake news detection. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing.

[42]

Seoh, R.; Birle, I.; Tak, M.; Chang, H.-S.; Pinette, B.; and Hough, A. 2021. Open Aspect Target Sentiment Classification with Natural Language Prompts. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 6311-6322.

[43]

Shaw, P.; Uszkoreit, J.; and Vaswani, A. 2018. Self-Attention with Relative Position Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers).

[44]

Snell, J.; Swersky, K.; and Zemel, R. 2017. Prototypical networks for few-shot learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems, 4080-4090.

[45]

Tian, L.; Zhang, X.; and Lau, J. H. 2021. Rumour Detection via Zero-Shot Cross-Lingual Transfer Learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 603-618. Springer.

[46]

Winata, G. I.; Madotto, A.; Lin, Z.; Liu, R.; Yosinski, J.; and Fung, P. 2021. Language Models are Few-shot Multilingual Learners. In Proceedings of the 1st Workshop on Multilingual Representation Learning, 1-15.

[47]

Wu, K.; Yang, S.; and Zhu, K. Q. 2015. False rumors detection on sina weibo by propagation structures. In 2015 IEEE 31st international conference on data engineering. IEEE.

[48]

Yang, F.; Liu, Y.; Yu, X.; and Yang, M. 2012. Automatic detection of rumor on sina weibo. In Proceedings of the ACM SIGKDD workshop on mining data semantics, 1-7.

[49]

Yang, Z.; Ma, J.; Chen, H.; Lin, H.; Luo, Z.; and Chang, Y. 2022. A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics, 2608-2621.

[50]

Yao, Y.; Rosasco, L.; and Caponnetto, A. 2007. On early stopping in gradient descent learning. Constructive Approximation, 26(2): 289-315.

[51]

Yu, F.; Liu, Q.; Wu, S.; Wang, L.; and Tan, T. 2017. A Convolutional Approach for Misinformation Identification. In IJCAI.

[52]

Zhao, M.; and Schutze, H. 2021. Discrete and Soft Prompting for Multilingual Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

[53]

Zhao, Z.; Resnick, P.; and Mei, Q. 2015. Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th international conference on world wide web, 1395-1405.

[54]

Zubiaga, A.; Aker, A.; Bontcheva, K.; Liakata, M.; and Procter, R. 2018. Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR).

Cited By

Abdali SShaham SKrishnamachari B(2024)Multi-modal Misinformation Detection: Approaches, Challenges and OpportunitiesACM Computing Surveys10.1145/369734957:3(1-29)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3697349
Imran MChatterjee PDamevski KRoychoudhury APaiva AAbreu RStorey M(2024)Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639223(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639223
Luo YLi YWen DLan LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Message Injection Attack on Rumor Detection under the Black-Box Evasion Setting Using Large Language ModelProceedings of the ACM Web Conference 202410.1145/3589334.3648139(4512-4522)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648139
Show More Cited By

Recommendations

Rumor Gauge: Predicting the Veracity of Rumors on Twitter
Special Issue on KDD 2016 and Regular Papers

The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated ...
Multimodal Zero-Shot Hateful Meme Detection
WebSci '22: Proceedings of the 14th ACM Web Science Conference 2022

Facebook has recently launched the hateful meme detection challenge, which garnered much attention in academic and industry research communities. Researchers have proposed multimodal deep learning classification methods to perform hateful meme ...
Using TwitterTrails.com to Investigate Rumor Propagation
CSCW'15 Companion: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing

Social media have become part of modern news reporting, used by journalists to spread information and find sources, or as a news source by individuals. The quest for prominence and recognition on social media sites like Twitter can sometimes eclipse ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence

February 2023

16496 pages

ISBN:978-1-57735-880-0

Copyright © 2023 Association for the Advancement of Artificial Intelligence.

Sponsors

Association for the Advancement of Artificial Intelligence

Publisher

AAAI Press

Publication History

Published: 07 February 2023

Qualifiers

Research-article
Research
Refereed limited

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Abdali SShaham SKrishnamachari B(2024)Multi-modal Misinformation Detection: Approaches, Challenges and OpportunitiesACM Computing Surveys10.1145/369734957:3(1-29)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3697349
Imran MChatterjee PDamevski KRoychoudhury APaiva AAbreu RStorey M(2024)Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639223(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639223
Luo YLi YWen DLan LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Message Injection Attack on Rumor Detection under the Black-Box Evasion Setting Using Large Language ModelProceedings of the ACM Web Conference 202410.1145/3589334.3648139(4512-4522)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648139
Zhang HLiu XYang QYang YQi FQian SXu CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)T3RD: Test-Time Training for Rumor Detection on Social MediaProceedings of the ACM Web Conference 202410.1145/3589334.3645443(2407-2416)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645443
Lin HLuo ZGao WMa JWang BYang RChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language ModelsProceedings of the ACM Web Conference 202410.1145/3589334.3645381(2359-2370)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645381
Ma JDai JLiu YHan MAi CFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Contrastive Learning for Rumor Detection via Fitting Beta Mixture ModelProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615138(4160-4164)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615138

View Options

View options

Media

Figures

Other

Tables

View Table of Contents