Skip to main content
SpringerLink
Log in

Text Stream to Temporal Network - A Dynamic Heartbeat Graph to Detect Emerging Events on Twitter

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10938))

Included in the following conference series:

Abstract

Huge mounds of data are generated every second on the Internet. People around the globe publish and share information related to real-world events they experience every day. This provides a valuable opportunity to analyze the content of this information to detect real-world happenings, however, it is quite challenging task. In this work, we propose a novel graph-based approach named the Dynamic Heartbeat Graph (DHG) that not only detects the events at an early stage, but also suppresses them in the upcoming adjacent data stream in order to highlight new emerging events. This characteristic makes the proposed method interesting and efficient in finding emerging events and related topics. The experiment results on real-world datasets (i.e. FA Cup Final and Super Tuesday 2012) show a considerable improvement in most cases, while time complexity remains very attractive.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aiello, L.M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Göker, A., Kompatsiaris, I., Jaimes, A.: Sensing trending topics in twitter. IEEE Trans. Multimed. 15(6), 1268–1282 (2013)

    Article  Google Scholar 

  2. Benhardus, J., Kalita, J.: Streaming trend detection in twitter. Int. J. Web Based Communities 9(1), 122–139 (2013)

    Article  Google Scholar 

  3. Buntain, C.: Discovering credible events in near real time from social media streams. In: Proceedings of the 24th International Conference on World Wide Web, pp. 481–485. ACM, New York (2015)

    Google Scholar 

  4. Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Quantifying controversy on social media. ACM Trans. Soc. Comput. 1(1), 3 (2018)

    Article  Google Scholar 

  5. Giannis Nikolentzos, P.M.a.V.: Matching node embedding for graph similarity. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), pp. 2429–2435 (2017)

    Google Scholar 

  6. Johansson, F., Jethava, V., Dubhashi, D., Bhattacharyya, C.: Global graph kernels using geometric embeddings. In: Proceedings of the 31st International Conference on Machine Learning, ICML 2014, Beijing, China, 21–26 June 2014 (2014)

    Google Scholar 

  7. Johansson, F.D., Dubhashi, D.: Learning with similarity functions on graphs using matchings of geometric embeddings. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 467–476. ACM (2015)

    Google Scholar 

  8. Nguyen, D.T., Jung, J.E.: Real-time event detection for online behavioral analysis of big social data. Future Gener. Comput. Syst. 66, 137–145 (2017)

    Article  Google Scholar 

  9. Shabunina, E., Pasi, G.: A graph-based approach to ememes identification and tracking in social media streams. Knowl. Based Syst. 139, 108–118 (2018)

    Article  Google Scholar 

  10. Velampalli, S., Eberle, W.: Novel graph based anomaly detection using background knowledge. In: Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, pp. 538–543 (2017)

    Google Scholar 

  11. Yanardag, P., Vishwanathan, S.: Deep graph kernels. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1365–1374. ACM (2015)

    Google Scholar 

  12. Yanardag, P., Vishwanathan, S.: A structural smoothing framework for robust graph comparison. In: Advances in Neural Information Processing Systems, pp. 2134–2142 (2015)

    Google Scholar 

  13. Yao, Y., Holder, L.B.: Detecting concept drift in classification over streaming graphs. In: KDD Workshop on Mining and Learning with Graphs (MLG), San Francisco, CA, 14 August 2016, pp. 2134–2142 (2016)

    Google Scholar 

  14. Zhou, D., Chen, L., He, Y.: An unsupervised framework of exploring events on twitter: filtering, extraction and categorization. In: Proceedings of 29th AAAI Conference on Artificial Intelligence, pp. 2468–2475. AAAI, USA (2015)

    Google Scholar 

  15. Zhou, X., Chen, L.: Event detection over twitter social media streams. VLDB J. Int. J. Very Larg. Data Bases 23(3), 381–400 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan

    Zafar Saeed & Rabeeh Ayaz Abbasi

  2. Advanced Analytics Institute, University of Technology Sydney, Sydney, Australia

    Zafar Saeed, Muhammad Imran Razzak & Guandong Xu

  3. Institute of Information Technology, Quaid-i-Azam University, Islamabad, Pakistan

    Abida Sadaf

Authors
  1. Zafar Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rabeeh Ayaz Abbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abida Sadaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Imran Razzak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guandong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guandong Xu .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saeed, Z., Abbasi, R.A., Sadaf, A., Razzak, M.I., Xu, G. (2018). Text Stream to Temporal Network - A Dynamic Heartbeat Graph to Detect Emerging Events on Twitter. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10938. Springer, Cham. https://doi.org/10.1007/978-3-319-93037-4_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93037-4_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93036-7

  • Online ISBN: 978-3-319-93037-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation