Cluster Computing

, Volume 22, Supplement 4, pp 8483–8491 | Cite as

Research on hot news discovery model based on user interest and topic discovery

  • Jianting LiEmail author
  • Xiao Ma


In order to find a way to process network data and discover hot news based on user’s interest and topic, through research on hot news discovery algorithm, a double-layer text clustering model based on density clustering strategy and Single-pass strategy is proposed. In view of the huge network data characteristics, DBSCAN algorithm is firstly used to cluster the single-crawled network data into small-scale clusters. Then, the Single-pass strategy is used to perform incremental clustering on the micro-classes to create the topic classes. In the hot news part of the network, the media and the user’s attention to the topic is combined to design a model. The heat quantization formula is obtained. Based on the research of related technologies, a network hot topic detection model is designed and implemented by using web crawler, news discovery and hot news discovery technology. By comparing the two-layer model used in the model with the traditional Single-pass strategy, the feasibility of the two-layer model is verified, and the discovery of network hot news is realized.


Web crawler Text clustering Interest and topic discovery Hot news discovery 



The authors acknowledge the Natural Science Research Plan in Educational Commission of Shaanxi Province of China (Program No. 2013JK1141).


  1. 1.
    Kausar, M.A., Dhaka, S., Singh, S.K.: Web crawler: a review. Int. J. Comput. Appl. 63(2), 23–35 (2013)Google Scholar
  2. 2.
    Rabotnov, Y.: Equilibrium of an elastic medium with after-effect. Fract. Calculus Appl. Anal. 17(3), 684–696 (2014)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Bouarara, H.A., Hamou, R.M., Amine, A.: Text clustering using distances combination by social bees: towards 3D visualisation aspect. Int. J. Inf. Retr. Res. 4(3), 34–53 (2014)Google Scholar
  4. 4.
    Tran, T., Vo, B., Le, T.T.N., Nguyen, N.T.: Text clustering using frequent weighted utility item sets. Cybern. Syst. 48(3), 193–209 (2017)CrossRefGoogle Scholar
  5. 5.
    Kriegel, H.P., Ntoutsi, E.: Clustering high dimensional data: examining differences and commonalities between subspace clustering and text clustering-A position paper. ACM SIGKDD Explor. Newsl. 15(2), 1–8 (2014)CrossRefGoogle Scholar
  6. 6.
    Chu, L., Zhang, Y., Li, G., Wang, S., Zhang, W., Huang, Q.: Effective multimodality fusion framework for cross-media topic detection. Trans. Circuit Syst. Video Technol. 26(3), 556–569 (2016)CrossRefGoogle Scholar
  7. 7.
    Corbett, J., Neal, R.A., Lunt, H.C., Tipton, M.J.: Adaptation to heat and exercise performance under cooler conditions: a new hot topic. Sports Med. 44(10), 1323–1331 (2014)CrossRefGoogle Scholar
  8. 8.
    Laupland, K.B., Valiquette, L.: Management of severe infections: A time to keep a cool head or a hot topic for clinical trials. Can. J. Infect. Dis. Med. Microbiol. 25(1), 9–10 (2014)CrossRefGoogle Scholar
  9. 9.
    Lu, J., Zhao, J., Cao, F.: Extended feed forward neural networks with random weights for face recognition. Neurocomputing 13(6), 96–102 (2014)CrossRefGoogle Scholar
  10. 10.
    Hu, Y.: Clustering-based hot topic detecting in chinese microblog. TELKOMNIKA Indones. J. Electr. Eng. 12(3), 2096–2103 (2014)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of InformationXi’an University of Finance and EconomicsXi’anChina
  2. 2.Experimental and Training CentresXi’an University of Finance and EconomicsXi’anChina

Personalised recommendations