News Dissemination on Twitter and Conventional News Channels

Agrima Seth, Shraddha Nayak, Josiane Mothe, Sangeeta Jadhay


Big Data is "things that one can do at a large scale that cannot be done at a small one". Analyzing flows of news events that happen worldwide falls in the scope of Big Data. Twitter has emerged as a valuable source of information where users post their thoughts on news events at a huge scale. At the same time traditional media channels also produce huge amount of data. This paper presents means to compare the propagation of the same news topic through Twitter and news articles, both important yet varied sources. We present visual means based on maps to make it possible to visualize the flow of information at different level of temporal granularity. We also provide an example and how the flow can be interpreted.


  1. Allan, J., Carbonell, J. G., Doddington, G., Yamron, J., Yang, Y., 1998. Topic detection and tracking pilot study final report. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop (pp. 194-218). DARPA.
  2. Allan, J., Papka, R., Lavrenko, V., 1998. On-line new event detection and tracking. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 37-45). ACM.
  3. Bergsma, S., Dredze, M., Van Durme, B., Wilson, T., Yarowsky, D., 2013. Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter, In Proceedings of NAACL-HLT 2013 (pp. 1010-1019). Association for Computational Linguistics.
  4. Dredze, M., Paul, M. J., Bergsma, S., & Tran, H., 2013. Carmen: A twitter geolocation system with applications to public health. In AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI), pp. 20-24.
  5. Fiscus, J. G., Doddington, G. R., 2002. Topic detection and tracking evaluation overview. In Topic detection and tracking (pp.17-31). Springer US.
  6. Fung, G. P. C., Yu, J. X., Yu, P. S., Lu, H., 2005. Parameter free bursty events detection in text streams. In Proceedings of the 31st international conference on Very large data bases (pp. 181-192). VLDB Endowment.
  7. Goeuriot, L., Mothe, J., Mulhem, P., Murtagh, F., & SanJuan, E. (2016). Overview of the CLEF 2016 Cultural micro-blog Contextualization Workshop. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 371- 378). Springer International Publishing.
  8. Gonzalez, R., Figueroa, G., Chen, Y. S., 2012. Tweolocator: a non-intrusive geographical locator system for twitter. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on LocationBased Social Networks (pp. 24-31). ACM.
  9. Hecht, B., Hong, L., Suh, B., Chi, E. H., 2011. Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 237-246). ACM.
  10. Khan, K., Baharudin, B., Khan, A., Ullah, A., 2014. Mining opinion components from unstructured reviews: A review. In Journal of King Saud University-Computer and Information Sciences, 26(3), 258-275.
  11. Kleinberg, J., 2003. Bursty and hierarchical structure in streams. In Data Mining and Knowledge Discovery, 7(4), 373-397.
  12. Kraiem, M. B., Feki, J., Khrouf, K., Ravat, F., & Teste, O. (2015). Modeling and OLAPing social media: the case of Twitter. Social Network Analysis and Mining, 5(1), 47.
  13. Lanagan, J., Smeaton, A. F., 2011. Using twitter to detect and tag important events in live sports. In Artificial Intelligence (pp. 542-545). Association for the Advancement of Artificial Intelligence.
  14. Lehmann, J., Gonçalves, B., Ramasco, J. J., Cattuto, C., 2012. Dynamical classes of collective attention in twitter. In Proceedings of the 21st international conference on World Wide Web (pp. 251-260). ACM.
  15. Murtagh, F., Ganz, A., McKie, S., Mothe, J., & Englmeier, K. (2010). Tag clouds for displaying semantics: the case of filmscripts. Information Visualization, 9(4), 253-262.
  16. Oussalah, M.; Bhat, F.; Challis, K.; and Schnier, T. 2012. A software architecture for twitter collection, search and geolocation services.Knowledge-Based Systems.
  17. Sakaki, T., Okazaki, M., Matsuo, Y., 2010. Tweet analysis for real-time event detection and earthquake reporting system development. In IEEE Transactions on Knowledge and Data Engineering, 25(4), 919-931.
  18. Thi Bich Ngoc Hoang, Josiane Mothe, Véronique Moriceau. Predicting Locations in Tweets. 2017. Computational Linguistics and Intelligent Text Processing, Budapest, Hungary.
  19. Vasudevan, S. Z. V., Wickramasuriya, J., Zhong, L., 2013. Is twitter a good enough social sensor for sports TV? In IEEE International Conference Pervasive Computing and Communications Workshops. PERCOM Workshops.
  20. Zhao, S., Zhong, L., Wickramasuriya, J., Vasudevan, V., 2011. Human as real-time sensors of social and physical events: A case study of twitter and sports games. arXiv preprint arXiv:1106.4300.

Paper Citation

in Harvard Style

Seth A., Nayak S., Mothe J. and Jadhay S. (2017). News Dissemination on Twitter and Conventional News Channels . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 43-52. DOI: 10.5220/0006264100430052

in Bibtex Style

author={Agrima Seth and Shraddha Nayak and Josiane Mothe and Sangeeta Jadhay},
title={News Dissemination on Twitter and Conventional News Channels},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - News Dissemination on Twitter and Conventional News Channels
SN - 978-989-758-247-9
AU - Seth A.
AU - Nayak S.
AU - Mothe J.
AU - Jadhay S.
PY - 2017
SP - 43
EP - 52
DO - 10.5220/0006264100430052