Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: WWW, pp. 675–684. ACM (2011)
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)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
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)
Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: IJCAI (2016)
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)
Hu, X., Tang, J., Gao, H., Liu, H.: Social spammer detection with sentiment information. In: ICDM, pp. 180–189. IEEE (2014)
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)
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)
Wu, L., Li, J., Hu, X., Liu, H.: Gleaning wisdom from the past: early detection of emerging rumors in social media. In: SDM (2016)
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)
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)
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)
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)
Greff, K., Srivastava, R.K., KoutnÃk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2016)
Rosenberg, A.: Classifying skewed data: importance weighting to optimize average recall. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, T., Chen, H., Li, X. (2018). Rumor Detection via Recurrent Neural Networks: A Case Study on Adaptivity with Varied Data Compositions. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-04503-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04502-9
Online ISBN: 978-3-030-04503-6
eBook Packages: Computer ScienceComputer Science (R0)