Learning to Explore Spatio-temporal Impacts for Event Evaluation on Social Media

  • Chung-Hong Lee
  • Hsin-Chang Yang
  • Wei-Shiang Wen
  • Cheng-Hsun Weng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)


Due to the explosive growth of social-media applications, enabling event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past microblogs for tackling emerging real-world events, in this work we utilize the social-media messages to characterize events through their contents and spatio-temporal features for relatedness analysis. Several essential features of each detected event dataset have been extracted for event formulation by performing content analysis, spatial analysis, and temporal analysis. This allows our approach compare the new event vector with existing event vectors stored in the event-data repository for evaluation of event relatednesss, by means of validating spatio-temporal feature factors involved in the event evolution. Through the developed algorithms for computing event relatedness, in our system the ranking of related events can be computed, allowing for predicting possible evolution and impacts of the event. The developed system platform is able to immediately evaluate the significantly emergent events, in order to achieve real-time knowledge discovery of disastrous events.


Stream mining data mining event detection social networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Becker, H., et al.: Learning Similarity Metrics for Event Identification in Social Media. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, New York, USA (2010)Google Scholar
  2. 2.
    Becker, H., et al.: Beyond Trending Topics: Real-World Event Identification on Twitter. In: Proceedings of the 25th ACM AAAI International Conference on Association for the Advancement of Artificial Intelligence, San Francisco, USA (2011)Google Scholar
  3. 3.
    Cunha, E., et al.: Analyzing the Dynamic Evolution of Hashtags on Twitter: A Language-Based Approach. In: Proceedings of the Workshop on Languages in Social Media, Portland, Oregon (2011)Google Scholar
  4. 4.
    Choudhury, M.D., et al.: Birds of a Feather: Does User Homophily Impact Information Diffusion in Social Media? In: Proceedings of the Computing Research Repository (2010)Google Scholar
  5. 5.
    Lee, C.-H., Wu, C.-H., Chien, T.-F.: BursT: A Dynamic Term Weighting Scheme for Mining Microblogging Messages. In: Liu, D. (ed.) ISNN 2011, Part III. LNCS, vol. 6677, pp. 548–557. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Lee, C.H., Chien, T.F., Yang, H.C.: DBHTE: A Novel Algorithm for Extracting Real-time Microblogging Topics. In: Proceedings of the 23rd International Conference on Computer Applications in Industry and Engineering, Las Vegas, USA (2010)Google Scholar
  7. 7.
    Lee, C.H., Yang, H.C., Chien, T.F., Wen, W.S.: A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs. In: Proceedings of the IEEE International Conference on Advances in Social Network Analysis and Mining, Kaohsiung, Taiwan, July 25-27 (2011)Google Scholar
  8. 8.
    Lee, C.H., Wen, W.S., Yang, H.C.: Mining Twitter Streams for Evaluating Event Relatedness Using a Density Based Clustering Approach. In: Proceedings of the 27th International Conference on Computer and their Applications, Las Vegas, USA, March 12-14 (2012)Google Scholar
  9. 9.
    Leskovec, J.: Social Media Analytics: Tracking, Modeling and Predicting the Flow of Information Through Networks. In: Proceedings of the 20th ACM WWW International Conference on World Wide Web, Hyderabad, India (2011)Google Scholar
  10. 10.
    Lin, C.X., et al.: Inferring the Diffusion and Evolution of Topics in Social Communities. In: Proceedings of the 5th ACM SNAKDD International Workshop on Social Network Mining and Analysis, San Diego, CA, USA (2011)Google Scholar
  11. 11.
    Nomoto, T.: Two-Tier Similarity Model for Story Link Detection. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada (2010)Google Scholar
  12. 12.
    Nallapati, R., Allan, J.: Capturing Term Dependencies Using a Language Model Based on Sentence Trees. In: Proceedings of the 8th International Conference on Information and Knowledge Management, McLean, Virginia, USA (2002)Google Scholar
  13. 13.
    Nallapati, R.: Semantic Language Models for Topic Detection and Tracking. In: Proceedings of the International Conference on the North American Chapter of the Association for Computational Linguistics on Human Language Technology: HLT-NAACL 2003 Student Research Workshop, Edmonton, Canada, vol. 3 (2003)Google Scholar
  14. 14.
    Wang, L., Li, F.: Story Link Detection Based on Event Words. In: Gelbukh, A. (ed.) CICLing 2011, Part II. LNCS, vol. 6609, pp. 202–211. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Shah, C., et al.: Representing Documents with Named Entities for Story Link Detection (SLD). In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, Arlington, Virginia, USA (2006)Google Scholar
  16. 16.
    Štajner, T., Grobelnik, M.: Story Link Detection with Entity Resolution. In: Proceedings of the 8th ACM WWW International Conference on World Wide Web Semantic Search Workshop, Madrid, Spain (2009)Google Scholar
  17. 17.
    Tang, X., Yang, C.C.: Following the Social Media: Aspect Evolution of Online Discussion. In: Salerno, J., Yang, S.J., Nau, D., Chai, S.-K. (eds.) SBP 2011. LNCS, vol. 6589, pp. 292–300. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Yang, C.C., et al.: Discovering Event Evolution Graphs from News Corpora. Proceedings of the IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 39, 850–863 (2009)CrossRefGoogle Scholar
  19. 19.
    Zhao, Q., et al.: Temporal and Information Flow Based Event Detection from Social Text Streams. In: Proceedings of the 22nd International Conference on Artificial Intelligence, Vancouver, British Columbia, Canada, vol. 2 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chung-Hong Lee
    • 1
  • Hsin-Chang Yang
    • 2
  • Wei-Shiang Wen
    • 1
  • Cheng-Hsun Weng
    • 1
  1. 1.Dept of Electrical EngineeringNational Kaohsiung University of Applied SciencesKaohsiungTaiwan
  2. 2.Dept of Information ManagementNational University of KaohsiungKaohsiungTaiwan

Personalised recommendations