Smart and Incremental Model to Build Clustered Trending Topics of Web Documents

  • Mona A. Abou-OfEmail author
  • Hassan M. SaadEmail author
  • Saad M. DarwishEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


The abstract Social media trends, which have become more popular nowadays, introduce a rich hub of a broad spectrum of topics. It is of great importance to track emerging related topics when major events occur. The source of such information would be available not only through social portals but also through news, articles and web portals. All this information is aggregated together, by the proposed news aggregator model, to be useful for retrieving the recent popular trends of a certain category or country. The proposed model addresses the identification of semantically related topics from user preferences and favorites that are added manually by the user. Their textual contexts are acquired from the news search and then a clustering technique is applied followed by tracking of trending topics in term space. By quantitative experiments on manually annotated trends, we compared the model with two other well-known algorithms, using three different online datasets. The presented results demonstrate that the model reliably achieves a better entropy and F-measure, and so outperforms the two other mentioned algorithms.


News aggregator WordNet Detecting and tracking topics Semantic similarity Text mining Summarization NLP Web mining Incremental clustering K-Means 


  1. 1.
    Fuchs, S., Borth, D., Ulges, A.: Trending topic aggregation by news-based context modeling. In: Proceedings of the 39th Annual German Conference, Advances in Artificial Intelligence, pp. 162–168. Springer, Cham (2016)Google Scholar
  2. 2.
    Mirhosseini, M.: A clustering approach using a combination of the gravitational search algorithm and k-harmonic means and its application in text document clustering. Turk. J. Electr. Eng. Comput. Sci. 25, 1251–1262 (2016)CrossRefGoogle Scholar
  3. 3.
    Sapul, M.S.C., Aung, T.H., Jiamthapthaksin, R.: Trending topic discovery of Twitter Tweets using clustering and topic modeling algorithms. In: Proceedings of 2017 14th International Joint Conference on Computer Science and Software Engineering, Thailand. IEEE (2017)Google Scholar
  4. 4.
    Zhang, Y., Ruan, X., Wang, H., He, S.: Twitter trends manipulation: a first look inside the security of Twitter trending. IEEE Trans. Inf. Forensics Secur. 12, 144–156 (2016)Google Scholar
  5. 5.
    Georgiou, T., El Abbadi, A., Yan, X.: Privacy-preserving community-aware trending topic detection in online social media. In: Chap. 11 of DBSec 2017: Data and Applications Security and Privacy XXXI, pp. 205–224. Springer, Cham (2017)Google Scholar
  6. 6.
    Muliawati, T., Murfi, H.: Eigenspace-based fuzzy c-means for sensing trending topics in Twitter. In: AIP Conference Proceedings, Indonesia, vol. 1862, no. 1 (2017)Google Scholar
  7. 7.
    Recalde, L., Nettleton, D.F., Baeza-Yates, R.: Detection of trending topic communities: bridging content creators and distributors. In: Proceedings of the 28th ACM Conference on Hypertext and Social Media, Prague, Czech Republic, pp. 205–213. ACM (2017)Google Scholar
  8. 8.
    Georgiou, T., El Abbadi, A., Yan, X.: Extracting topics with focused communities for social content recommendation. In: Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing, USA. ACM (2017)Google Scholar
  9. 9.
    Morchid, M., Josselin, D., Portilla, Y., Dufour, R., Linarès, G.: A topic modeling based representation to detect tweet locations. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, France (2015)Google Scholar
  10. 10.
    Wang, J., Zelenyuk, A., Imre, D., Mueller, K.: Big data management with incremental k-means trees–GPU-accelerated construction and visualization. Inform. Open Access J. 4, 24 (2017)Google Scholar
  11. 11.
    Islam, M.N., Seera, M., Loo, C.K.: A robust incremental clustering-based facial feature tracking. Appl. Soft Comput. 53, 34–44 (2017)CrossRefGoogle Scholar
  12. 12.
    Butnaru, A.M., Ionescu, R.T., Hristea, F.: ShotgunWSD: an unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing. In: Proceedings of EACL 2017, Romania (2017)Google Scholar
  13. 13.
    Miller, G.: WordNet: a lexical database for English. Commun. ACM 38, 39–41 (1995)CrossRefGoogle Scholar
  14. 14.
    Corra, E., Lopes, A., Amancio, D.: Word sense disambiguation. Inf. Sci.–Inform. Comput. Sci. Intell. Syst. Appl.: Int. J. 442(C), 103–113 (2018)Google Scholar
  15. 15.
    Shamir, R., Duchin, Y., Kim, J., Sapiro, G., Harel, N.: Continuous dice coefficient: a method for evaluating probabilistic segmentations. In: Proceedings of Radiotherapy and Oncology, Barcelona, Spain, vol. 127. Elsevier (2018)Google Scholar
  16. 16.
    Chan, G., Ong, K., Wong, T., Chow, L.: Intelligent context-based pattern matching approaches to enhance decision making. In: Proceedings of International Conference on Computational Science and Its Applications, vol. 10960, pp. 485–497. Springer, Cham (2018)Google Scholar
  17. 17.
    Lee, E.: Partisan intuition belies strong, institutional consensus and wide Zipf’s law for voting blocs in US Supreme Court. J. Stat. Phy. 173, 1722–1733 (2018)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Vu, D., Dao, N., Cho, S.: Downlink sum-rate optimization leveraging Hungarian method in fog radio access networks. In: Proceedings of International Conference on Information Networking (ICOIN). IEEE, Thailand (2018)Google Scholar
  19. 19.
  20. 20.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Pharos University in AlexandriaAlexandriaEgypt
  2. 2.Institute of Graduate Studies and ResearchesAlexandria UniversityAlexandriaEgypt

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