Abstract
Recently a lot of progress has been made in rumor modeling and rumor detection for micro-blogging streams. However, existing automated methods do not perform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the early phase of a rumor. In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights on the credibility of each tweets. We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model. Our extensive experiments show a clearly improved classification performance within the critical very first hours of a rumor. For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor lifetime.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
- 1.
Deutsche Welle: http://bit.ly/2qZuxCN.
- 2.
details are listed in the Appendix.
- 3.
the terminology subless indicates an event with no sub-events for short.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Ahmed, F., Abulaish, M.: An MCL-based approach for spam profile detection in online social networks. In: Proceedings of TrustCom, pp. 602–608. IEEE (2012)
Allport, G.W., Postman, L.: The Psychology of Rumor (1947)
Bao, Y., Yi, C., Xue, Y., Dong, Y.: A new rumor propagation model and control strategy on social networks. In: Proceedings of ICWSM, pp. 1472–1473. ACM (2013)
Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of ACL, pp. 36–44 (2010)
Borge-Holthoefer, J., Moreno, Y.: Absence of influential spreaders in rumor dynamics. Phys. Rev. E 85(2), 026116 (2012)
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of WWW, pp. 675–684. ACM (2011)
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)
Conti, M., Lain, D., Lazzeretti, R., Lovisotto, G., Quattrociocchi, W.: It’s always April fools’ day! on the difficulty of social network misinformation classification via propagation features. CoRR, abs/1701.04221 (2017)
Dhingra, B., Zhou, Z., Fitzpatrick, D., Muehl, M., Cohen, W.W.: Tweet2Vec: character-based distributed representations for social media. arXiv preprint arXiv:1605.03481 (2016)
Friggeri, A., Adamic, L.A., Eckles, D., Cheng, J.: Rumor cascades (2014)
Gupta, A., Kumaraguru, P., Castillo, C., Meier, P.: TweetCred: real-time credibility assessment of content on Twitter. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 228–243. Springer, Cham (2014). doi:10.1007/978-3-319-13734-6_16
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jin, F., Dougherty, E., Saraf, P., Cao, Y., Ramakrishnan, N.: Epidemiological modeling of news and rumors on Twitter. In: Proceedings of SNA-KDD (2013)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Kimmey, D.: Twitter event detection (2015)
Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: Proceedings of ICDM (2013)
Liu, X., Nourbakhsh, A., Li, Q., Fang, R., Shah, S.: Real-time rumor debunking on Twitter. In: Proceedings of CIKM, pp. 1867–1870. ACM (2015)
Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K.-F., Cha, M.: Detecting rumors from microblogs with recurrent neural networks
Ma, J., Gao, W., Wei, Z., Lu, Y., Wong, K.-F.: Detect rumors using time series of social context information on microblogging websites. In: Proceedings of CIKM (2015)
McMinn, A.J., Moshfeghi, Y., Jose, J.M.: Building a large-scale corpus for evaluating event detection on Twitter. In: Proceedings of CIKM (2013)
Meladianos, P., Nikolentzos, G., Rousseau, F., Stavrakas, Y., Vazirgiannis, M.: Degeneracy-based real-time sub-event detection in Twitter stream. In: Proceedings of ICWSM (2015)
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. ACM (2010)
Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of EMNLP (2011)
Seo, E., Mohapatra, P., Abdelzaher, T.: Identifying rumors and their sources in social networks. In: SPIE (2012)
Sunstein, C.R.: On Rumors: How Falsehoods Spread, Why we Believe Them, and What can be Done. Princeton University Press, Princeton (2014)
Tripathy, R.M., Bagchi, A., Mehta, S.: A study of rumor control strategies on social networks. In: Proceedings of CIKM, pp. 1817–1820. ACM (2010)
Vosoughi, S., Vijayaraghavan, P., Roy, D.: Tweet2Vec: learning tweet embeddings using character-level CNN-LSTM encoder-decoder. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1041–1044. ACM (2016)
Wang, A.H.: Don’t follow me: spam detection in Twitter. In: Proceedings of SECRYPT, pp. 1–10. IEEE (2010)
Wu, K., Yang, S., Zhu, K.Q.: False rumors detection on Sina Weibo by propagation structures. In: Proceedings of ICDE, pp. 651–662. IEEE (2015)
Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on Sina Weibo. In: Proceedings of MDS. ACM (2012)
Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of WWW (2015)
Zhou, C., Sun, C., Liu, Z., Lau, F.: A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630 (2015)
Acknowledgements
This work was partially funded by the German Federal Ministry of Education and Research (BMBF) under project GlycoRec (16SV7172) and K3 (13N13548).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendix A Time Period of an Event
The time period of a rumor event is hard to define. One reason is a rumor may be created for a long time and kept existing on Twitter, but it did not attract the crowd’s attention. However it can be triggered by other events after a uncertain time and suddenly spreads as a bursty event. E.g., a rumorFootnote 7 claimed that Robert Byrd was member of KKK. This rumor has been circulating in Twitter for a while. As shown in Fig. 6(a) that almost every day there were several tweets talking about this rumor. But this rumor was triggered by a picture about Robert Byrd kissing Hillary Clinton in 2016Footnote 8 and Twitter users suddenly noticed this rumor and it was bursted. And what we are really interested in is the tweets which are posted in hours around the bursty peak. We defined the hour with the most tweets’ volume as \(t_{max}\) and we want to detect the rumor event as soon as possible before its burst, so we define the time of the first tweet before \(t_{max}\) within 48 h as the beginning of this rumor event, marked as \(t_{0}\). And the end time of the event is defined as \(t_{end}=t_0+48\). We show the tweet volumes in Fig. 6 of the above rumor example.
tweet volume of the rumor event of Robert Byrd at full scale and after selected time period
Appendix B Full FeaturesTime Period of an Event
See Table 6.
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Nguyen, T.N., Li, C., Niederée, C. (2017). On Early-Stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10540. Springer, Cham. https://doi.org/10.1007/978-3-319-67256-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-67256-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67255-7
Online ISBN: 978-3-319-67256-4
eBook Packages: Computer ScienceComputer Science (R0)