Skip to main content

Rumor Verification on Social Media with Stance-Aware Recursive Tree

  • Conference paper
  • First Online:
Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

Abstract

Since rumors have affected real society harmfully, automatic rumor verification attracts much attention from researchers. Incorporating the stance-aware knowledge into rumor verification is a hot direction, because its great potential to boost verification performance has been revealed in many studies. However, existing methods are still limited by two problems, the existence of short retweets and fraud nodes. For short retweets, since it is hard to extract the semantic information from short retweets, modeling the stance between a tweet and its short retweets could carry out training noises. For fraud nodes, they might perturb the normal propagation structure of rumors, so the model could be misled to capture those wrong stance information. To mitigate them, we propose a Credibility and Stance Aware recursive Tree (CSATree) for rumor verification. Firstly, we utilize a self-attention mechanism and a multi-task learning module to explore the context of short retweets, which could help to enrich the semantics and stance information in short retweets. In detail, the context of short retweets refers to those retweets that respond to a same tweet, i.e., sibling nodes in a conversation tree. Secondly, we take the node credibility into account and adopt another novel attention mechanism to reduce the impact of fraud nodes. Experiments on two public datasets demonstrate that CSATree significantly outperforms the current best stance-aware model by around 9%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen, Y., Hu, L., Sui, J.: Text-based fusion neural network for rumor detection. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds.) KSEM 2019. LNCS (LNAI), vol. 11776, pp. 105–109. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29563-9_11

    Chapter  Google Scholar 

  2. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)

  3. Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Hoi, G.W.S., Zubiaga, A.: Semeval-2017 task 8: Rumoureval: determining rumour veracity and support for rumours. arXiv preprint arXiv:1704.05972 (2017)

  4. Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482–7491 (2018)

    Google Scholar 

  5. 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, vol. 34, pp. 8783–8790 (2020)

    Google Scholar 

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

  7. Kumar, S., Carley, K.M.: Tree LSTMS with convolution units to predict stance and rumor veracity in social media conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5047–5058 (2019)

    Google Scholar 

  8. 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, pp. 1173–1179 (2019)

    Google Scholar 

  9. Ma, J., Gao, W.: Debunking rumors on twitter with tree transformer. In: ACL (2020)

    Google Scholar 

  10. Ma, J., Gao, W., Wong, K.F.: Detect rumor and stance jointly by neural multi-task learning. In: Companion Proceedings of the the Web Conference 2018, pp. 585–593 (2018)

    Google Scholar 

  11. Ma, J., Gao, W., Wong, K.F.: Rumor detection on twitter with tree-structured recursive neural networks. Association for Computational Linguistics (2018)

    Google Scholar 

  12. Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: can we trust what we RT? In: Proceedings of the First Workshop on Social Media Analytics, pp. 71–79 (2010)

    Google Scholar 

  13. Pamungkas, E.W., Basile, V., Patti, V.: Stance classification for rumour analysis in twitter: Exploiting affective information and conversation structure. arXiv preprint arXiv:1901.01911 (2019)

  14. Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)

  15. Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)

  16. Veyseh, A.P.B., Ebrahimi, J., Dou, D., Lowd, D.: A temporal attentional model for rumor stance classification. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2335–2338 (2017)

    Google Scholar 

  17. Wei, P., Xu, N., Mao, W.: Modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity. arXiv preprint arXiv:1909.08211 (2019)

  18. Yu, J., Jiang, J., Khoo, L.M.S., Chieu, H.L., Xia, R.: Coupled hierarchical transformer for stance-aware rumor verification in social media conversations. Association for Computational Linguistics (2020)

    Google Scholar 

  19. Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R.: Detection and resolution of rumours in social media: a survey. ACM Comput. Surv. (CSUR) 51(2), 1–36 (2018)

    Article  Google Scholar 

  20. Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., Tolmie, P.: Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS One 11(3), e0150989 (2016)

    Google Scholar 

Download references

Acknowledgment

This work is supported by the National Key R&D Program of China under Grants (No. 2018YFB0204300).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhen Huang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Han, X., Huang, Z., Lu, M., Li, D., Qiu, J. (2021). Rumor Verification on Social Media with Stance-Aware Recursive Tree. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-82153-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82152-4

  • Online ISBN: 978-3-030-82153-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics