Exploiting Context for Rumour Detection in Social Media

  • Arkaitz ZubiagaEmail author
  • Maria Liakata
  • Rob Procter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)


Tools that are able to detect unverified information posted on social media during a news event can help to avoid the spread of rumours that turn out to be false. In this paper we compare a novel approach using Conditional Random Fields that learns from the sequential dynamics of social media posts with the current state-of-the-art rumour detection system, as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying the stance of a post to deem it a rumour but, instead, exploits context learned during the event. Our classifier has improved precision and recall over the state-of-the-art classifier that relies on querying tweets, as well as outperforming our best baseline. Moreover, the results provide evidence for the generalisability of our classifier.


Social media Rumour detection Breaking news Journalism 



This work has been supported by the PHEME FP7 project (grant No. 611233). Maria Liakata and Rob Procter were also supported by the Alan Turing Institute. We would also like to thank Queen Mary University of London for the use of its MidPlus computational facilities, which was supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1.


  1. 1.
    Allport, G.W., Postman, L.: An analysis of rumor. Publ. Opin. Q. 10(4), 501–517 (1946)CrossRefGoogle Scholar
  2. 2.
    Bazerli, G., Bean, T., Crandall, A., Coutin, M., Kasindi, L., Procter, R.N., Rodger, S., Saber, D., Slachmuijlder, L., Trewinnard, T.: Humanitarianism 2.0. Glob. Policy J. (2015).
  3. 3.
    Bontcheva, K., Derczynski, L., Funk, A., Greenwood, M.A., Maynard, D., Aswani, N.: TwitIE: an open-source information extraction pipeline for microblog text. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing. Association for Computational Linguistics (2013)Google Scholar
  4. 4.
    Cai, G., Wu, H., Lv, R.: Rumors detection in Chinese via crowd responses. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 912–917. IEEE (2014)Google Scholar
  5. 5.
    Cunningham, H., Maynard, D., Bontcheva, K.: Text Processing with Gate. Gateway Press CA, Murphys (2011)Google Scholar
  6. 6.
    Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Wong Sak Hoi, G., Zubiaga, A.: SemEval-2017 task 8: RumourEval: determining rumour veracity and support for rumours. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 69–76. Association for Computational Linguistics, Vancouver (2017)Google Scholar
  7. 7.
    Derczynski, L., Bontcheva, K., Lukasik, M., Declerck, T., Scharl, A., Georgiev, G., Osenova, P., Lobo, T.P., Kolliakou, A., Stewart, R., et al.: PHEME: computing veracity - the fourth challenge of big social data. In: Proceedings of the Extended Semantic Web Conference EU Project Networking session (ESCW-PN) (2015)Google Scholar
  8. 8.
    DiFonzo, N., Bordia, P.: Rumor, gossip and urban legends. Diogenes 54(1), 19–35 (2007)CrossRefGoogle Scholar
  9. 9.
    Giasemidis, G., Singleton, C., Agrafiotis, I., Nurse, J.R.C., Pilgrim, A., Willis, C., Greetham, D.V.: Determining the veracity of rumours on Twitter. In: Spiro, E., Ahn, Y.-Y. (eds.) SocInfo 2016. LNCS, vol. 10046, pp. 185–205. Springer, Cham (2016). doi: 10.1007/978-3-319-47880-7_12 CrossRefGoogle Scholar
  10. 10.
    Hamidian, S., Diab, M.T.: Rumor detection and classification for Twitter data. In: Proceedings of the Fifth International Conference on Social Media Technologies, Communication, and Informatics (SOTICS), pp. 71–77 (2015)Google Scholar
  11. 11.
    Hamidian, S., Diab, M.T.: Rumor identification and belief investigation on Twitter. In: Proceedings of NAACL-HLT, pp. 3–8 (2016)Google Scholar
  12. 12.
    Jin, Z., Cao, J., Zhang, Y., Luo, J.: News verification by exploiting conflicting social viewpoints in microblogs. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 2972–2978 (2016)Google Scholar
  13. 13.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML, vol. 1, pp. 282–289 (2001)Google Scholar
  14. 14.
    Liang, G., He, W., Xu, C., Chen, L., Zeng, J.: Rumor identification in microblogging systems based on users’ behavior. IEEE Trans. Comput. Soc. Syst. 2(3), 99–108 (2015)CrossRefGoogle Scholar
  15. 15.
    Liu, X., Nourbakhsh, A., Li, Q., Fang, R., Shah, S.: Real-time rumor debunking on Twitter. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1867–1870. ACM (2015)Google Scholar
  16. 16.
    Lukasik, M., Bontcheva, K., Cohn, T., Zubiaga, A., Liakata, M., Procter, R.: Using Gaussian processes for rumour stance classification in social media. arXiv preprint arXiv:1609.01962 (2016)
  17. 17.
    Lukasik, M., Cohn, T., Bontcheva, K.: Classifying tweet level judgements of rumours in social media. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2590–2595 (2015)Google Scholar
  18. 18.
    Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K.F., Cha, M.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 3818–3824 (2016)Google Scholar
  19. 19.
    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 the 24th ACM International on Conference on Information and Knowledge Management, pp. 1751–1754. ACM (2015)Google Scholar
  20. 20.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  21. 21.
    Müller, A.C., Behnke, S.: PyStruct: learning structured prediction in python. The J. Mach. Learn. Res. 15(1), 2055–2060 (2014)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Procter, R., Crump, J., Karstedt, S., Voss, A., Cantijoch, M.: Reading the riots: what were the police doing on Twitter? Polic. Soc. 23(4), 413–436 (2013)CrossRefGoogle Scholar
  23. 23.
    Procter, R., Vis, F., Voss, A.: Reading the riots on Twitter: methodological innovation for the analysis of big data. Int. J. Soc. Res. Methodol. 16(3), 197–214 (2013)CrossRefGoogle Scholar
  24. 24.
    Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1589–1599 (2011)Google Scholar
  25. 25.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 42–51. ACM (2009)Google Scholar
  26. 26.
    Starbird, K., Maddock, J., Orand, M., Achterman, P., Mason, R.M.: Rumors, false flags, and digital vigilantes: misinformation on Twitter after the 2013 Boston marathon bombing. In: Proceedings of iConference 2014 (2014)Google Scholar
  27. 27.
    Sutton, C., McCallum, A.: An introduction to conditional random fields. Mach. Learn. 4(4), 267–373 (2011)CrossRefzbMATHGoogle Scholar
  28. 28.
    Takayasu, M., Sato, K., Sano, Y., Yamada, K., Miura, W., Takayasu, H.: Rumor diffusion and convergence during the 3.11 earthquake: a Twitter case study. PLoS ONE 10(4), e0121443 (2015)CrossRefGoogle Scholar
  29. 29.
    Tolmie, P., Procter, R., Randall, D.W., Rouncefield, M., Burger, C., Wong Sak Hoi, G., Zubiaga, A., Liakata, M.: Supporting the use of user generated content in journalistic practice. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 3632–3644. ACM (2017)Google Scholar
  30. 30.
    Tolmie, P., Procter, R., Rouncefield, M., Liakata, M., Zubiaga, A.: Microblog analysis as a programme of work. arXiv preprint arXiv:1511.03193 (2015)
  31. 31.
    Tolosi, L., Tagarev, A., Georgiev, G.: An analysis of event-agnostic features for rumour classification in Twitter. In: ICWSM Workshop on Social Media in the Newsroom, pp. 151–158 (2016)Google Scholar
  32. 32.
    Webb, H., Burnap, P., Procter, R., Rana, O., Stahl, B., Williams, M., Housley, W., Edwards, A., Jirotka, M.: Digital wildfires: propagation, verification, regulation, and responsible innovation. ACM Trans. Inf. Syst. 34(3), 15:1–15:23 (2016)CrossRefGoogle Scholar
  33. 33.
    Wu, K., Yang, S., Zhu, K.Q.: False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 651–662. IEEE (2015)Google Scholar
  34. 34.
    Zeng, L., Starbird, K., Spiro, E.S.: # unconfirmed: classifying rumor stance in crisis-related social media messages. In: Tenth International AAAI Conference on Web and Social Media, pp. 747–750 (2016)Google Scholar
  35. 35.
    Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1395–1405. ACM (2015)Google Scholar
  36. 36.
    Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R.: Detection and resolution of rumours in social media: a survey. arXiv preprint arXiv:1704.00656 (2017)
  37. 37.
    Zubiaga, A., Ji, H., Knight, K.: Curating and contextualizing twitter stories to assist with social newsgathering. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 213–224. ACM (2013)Google Scholar
  38. 38.
    Zubiaga, A., Kochkina, E., Liakata, M., Procter, R., Lukasik, M.: Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. In: Proceedings of the International Conference on Computational Linguistics (COLING). Association for Natural Language Processing (ANLP) (2016)Google Scholar
  39. 39.
    Zubiaga, A., Liakata, M., Procter, R., Bontcheva, K., Tolmie, P.: Crowdsourcing the annotation of rumourous conversations in social media. In: Proceedings of the 24th International Conference on World Wide Web Companion, pp. 347–353. International World Wide Web Conferences Steering Committee (2015)Google Scholar
  40. 40.
    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), 1–29 (2016).

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of WarwickCoventryUK
  2. 2.Alan Turing InstituteLondonUK

Personalised recommendations