Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10855–10879 | Cite as

Visual topic discovering, tracking and summarization from social media streams

  • Zhao LuEmail author
  • Yu-Ru Lin
  • Xiaoxia Huang
  • Naixue Xiong
  • Zhijun Fang


Nowadays, microblogging has become popular, with hundreds of millions of short messages being posted and shared every minute on a variety of topics in social media such as Facebook, Twitter and Weibo. Many of such messages contain videos that captured particular events or moments in people’s life. In this work, we seek to automatically identify the video topics posted in the social media streams on Weibo. While Topic Detection and Tracking (TDT) task has been extensively studied in multimedia retrieval, automatically discovering, tracking and summarizing video topics from social media streams is still challenging due to short and noisy content, diverse and fast changing topics, and large data volume. In this paper, we propose a K-partite graph based approach to address these challenges. We introduce a K-partite graph representation to simultaneously model the relationships among videos contained in the Weibo streams, their textural features and visual features. We propose a novel joint clustering algorithm to capture global structure of the K-partite graph in a “relation cluster network” (RCN) where latent, meta-nodes are added to the network to represent video clusters. Based on this network we propose methods for tracking and summarizing the videos in streams through fusing various types of features and multiple ranking schemes. The experiment results based on a real dataset show the effectiveness of our method with significant improvement over baseline.


Topic detection and tracking Social media stream K-partite graph Multi-modality feature representation 



This research was supported in part by National Key Technology Support Program (No. 2015BAH01F02), in part by Science and Technology Commission of Shanghai Municipality (No.16511102702 and No.14DZ2260800). The authors gratefully acknowledge the support of Yu-Ru Lin’s Lab at University of Pittsburgh, supported in part by NSF grant #1423697, #1634944, and the CRDF and CIS. Any opinions, findings, and conclusions or recommendations expressed in this material do not necessarily reflect the views of the funding sources.


  1. 1.
    Cao J, Ngo C-W, Zhang Y-D, Li J-T (2011) Tracking web video topics: discovery, visualization, and monitoring. IEEE Trans. Circuits Syst. Video Technol. 21 (12):1835–1846CrossRefGoogle Scholar
  2. 2.
    Cao J, Ngo C-W, Zhang Y, Zhang D, Ma L (2010) Trajectory-based visualization of web video topics. In: Proceedings of the international conference on Multimedia 2010, pp 1639–1642Google Scholar
  3. 3.
    Cao J, Zhang Y, Ji R, Xie F, Su Y (2015) Web video topics discovery and structuralization with social network, NeurocomputingGoogle Scholar
  4. 4.
    Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux J-L (2015) Color image analysis by quaternion-type moments. J. Math. Imaging Vision 51(1):124–144MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Guille A, Favre C (2015) Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach[J]. Soc. Netw. Anal. Min. 5(4):1–18Google Scholar
  6. 6.
    Li C, Sun A, Datta A (2012) Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp 155–164Google Scholar
  7. 7.
    Li G, Zhang W, Pang J, Huang Q, Jiang S (2013) Online web-video topic detection and tracking with semisupervised learning. In: Advances in Multimedia Information Processing (PCM 2013), pp 750–759Google Scholar
  8. 8.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based Image Copy-move Forgery Detection Scheme. IEEE Trans. Inf. Forensics Secur. 10(3):507–518CrossRefGoogle Scholar
  9. 9.
    Liu L, Sun L, Rui Y, Shi Y, Yang S (2008) Web video topic discovery and tracking via bipartite graph reinforcement model. In: Proceedings of the 17th international conference on World Wide Web, pp 1009–1018Google Scholar
  10. 10.
    Long B, Wu X, Zhang ZM, Yu PS (2006) Unsupervised learning on k-partite graphs. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 317–326Google Scholar
  11. 11.
    Mohanta PP, Saha SK, Chanda B (2012) A model-based shot boundary detection technique using frame transition parameters. IEEE Trans. Multimedia 14 (1):223–233CrossRefGoogle Scholar
  12. 12.
    Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding, IEEE Transactions on Broadcasting. doi: 10.1109/TBC.2015.2419824
  13. 13.
    Pelleg D, Moore AW, et al. (2000) X-means: Extending k-means with efficient estimation of the number of clusters. In: ICML, pp 727–734Google Scholar
  14. 14.
    Ran L, Suzhi X, Yuanyuan R, Zhenfang Z (2014) A modified approach of hot topics found on micro-blog. In: Frontier and Future Development of Information Technology in Medicine and Education, pp 603–614Google Scholar
  15. 15.
    Shao J, Ma S, Lu W, Zhuang Y (2012) A unified framework for web video topic discovery and visualization. Pattern Recogn Lett 33(4):410–419CrossRefGoogle Scholar
  16. 16.
    Shao J, Yin W, Ma S, Zhuang Y (2010) Topic discovery of web video using star-structured k-partite graph. In: Proceedings of the international conference on Multimedia, pp 915–918Google Scholar
  17. 17.
    Shi SK, Li L (2012) A close-to-linear topic detection algorithm using relative entropy based relevance model and inverted indices retrieval. Int J Comput Intell Syst 5(4):735–744CrossRefGoogle Scholar
  18. 18.
    Tu H, Ding J (2012) An efficient clustering algorithm for microblogging hot topic detection. In: 2012 International Conference on Computer Science & Service System (CSSS), pp 738–741Google Scholar
  19. 19.
    Wang Z, Sun L, Wu C, Yang S (2015) Enhancing internetscale video service deployment using microblog-based prediction. IEEE Trans. Parallel Distrib. Syst. 26 (3):775–785CrossRefGoogle Scholar
  20. 20.
    Wanner F et al (2014) State-of-the-art report of visual analysis for event detection in text data streams. In: Computer Graphics Forum, vol 33Google Scholar
  21. 21.
    Xia Z, Wang X, Sun X, Liu Q, Xiong N (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl 75 (4):1947–1962CrossRefGoogle Scholar
  22. 22.
    Xiong N, Jia X, Yang LT, Vasilakos AV, Li Y, Pan Y (2010) A distributed efficient flow control scheme for multirate multicast networks. IEEE Trans. Parallel Distrib. Syst. 21(9):1254–1266CrossRefGoogle Scholar
  23. 23.
    Xiong N, Vasilakos AV, Yang LT, Song L, Pan Y, Kannan R, Li Y (2009) Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE J. Sel. Areas Commun. 27(4):495–509CrossRefGoogle Scholar
  24. 24.
    Zhai Y, Shah M (2005) Tracking news stories across different sources. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp 2–10Google Scholar
  25. 25.
    Zhang W, Chen T, Li G, Pang J, Huang Q, Gao W (2015) Fusing cross-media for topic detection by dense keyword groups. Neurocomputing 169:169–179CrossRefGoogle Scholar
  26. 26.
    Zhang Y, Li G, Chu L, Wang S, Zhang W, Huang Q (2013) Cross-media topic detection: a multi-modality fusion framework. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp 1–6Google Scholar
  27. 27.
    Zheng Y, Jeon B, Danhua D, Wu JQM, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J. Intell. Fuzzy Syst. 28(2):961–973Google Scholar
  28. 28.
    Zhou X, Chen L (2014) Event detection over twitter social media streams. VLDB J - Int J Very Large Data Bases 23(3):381–400MathSciNetCrossRefGoogle Scholar
  29. 29.
    Zhu T, Yu J (2014) A prerecognition model for hot topic discovery based on microblogging data. Sci. World J.:2014Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Technology, Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
  2. 2.School of Information SciencesUniversity of PittsburghPittsburghUSA
  3. 3.Department of Business and Computer ScienceSouthwestern Oklahoma State UniversityWeatherfordUSA
  4. 4.School of Electronic and Electrical EngineeringShanghai University of Engineering ScienceShanghaiChina

Personalised recommendations