Rumor Detection via Recurrent Neural Networks: A Case Study on Adaptivity with Varied Data Compositions

  • Tong ChenEmail author
  • Hongxu Chen
  • Xue Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)


Rumor detection is a meaningful research problem due to its significance in preventing potential threats to cyber security and social stability. With the recent popularity of recurrent neural networks (RNNs), the application of RNNs in rumor detection has resulted in promising results as RNNs can naturally blend into the task of language processing and sequential data modelling. However, since deep learning models require large data scale for training in order to extract sufficient distinctive patterns, their adaptivity with varied data compositions can become a challenge for real-life application scenarios where rumors are always the minority (outlier) in the streaming data. In this paper, we present a case study to investigate how the ratio of rumors in training data affects the calssification performance of RNN based rumor detection models and successfully address some issues on the model adaptivity.


Recurrent neural networks Rumor detection 


  1. 1.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: WWW, pp. 675–684. ACM (2011)Google Scholar
  2. 2.
    Chen, C., Wang, Y., Zhang, J., Xiang, Y., Zhou, W., Min, G.: Statistical features-based real-time detection of drifted twitter spam. IEEE Trans. Inf. Forensics Secur. 12(4), 914–925 (2017)CrossRefGoogle Scholar
  3. 3.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  4. 4.
    Chen, W., et al.: EEG-based motion intention recognition via multi-task RNNs. In: Proceedings of the 2018 SIAM International Conference on Data Mining, SIAM (2018)Google Scholar
  5. 5.
    Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: IJCAI (2016)Google Scholar
  6. 6.
    Grier, C., Thomas, K., Paxson, V., Zhang, M.: @spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 27–37. ACM (2010)Google Scholar
  7. 7.
    Hu, X., Tang, J., Gao, H., Liu, H.: Social spammer detection with sentiment information. In: ICDM, pp. 180–189. IEEE (2014)Google Scholar
  8. 8.
    Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: WWW, pp. 1395–1405. ACM (2015)Google Scholar
  9. 9.
    Sampson, J., Morstatter, F., Wu, L., Liu, H.: Leveraging the implicit structure within social media for emergent rumor detection. In: CIKM, pp. 2377–2382. ACM (2016)Google Scholar
  10. 10.
    Wu, L., Li, J., Hu, X., Liu, H.: Gleaning wisdom from the past: early detection of emerging rumors in social media. In: SDM (2016)Google Scholar
  11. 11.
    Ma, J., Gao, W., Wei, Z., Lu, Y., Wong, K.F.: Detect rumors using time series of social context information on microblogging websites. In: CIKM, pp. 1751–1754. ACM (2015)Google Scholar
  12. 12.
    Chen, T., Wu, L., Li, X., Zhang, J., Yin, H., Wang, Y.: Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. arXiv preprint arXiv:1704.05973 (2017)
  13. 13.
    Chen, H., Yin, H., Li, X., Wang, M., Chen, W., Chen, T.: People opinion topic model: opinion based user clustering in social networks. In: WWW Companion, International World Wide Web Conferences Steering Committee, pp. 1353–1359 (2017)Google Scholar
  14. 14.
    Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: ICDM, pp. 1103–1108. IEEE (2013)Google Scholar
  15. 15.
    Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Rosenberg, A.: Classifying skewed data: importance weighting to optimize average recall. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)Google Scholar

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Authors and Affiliations

  1. 1.The University of QueenslandBrisbaneAustralia

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