Skip to main content
SpringerLink
Log in

A Topic Detection and Visualisation System on Social Media Posts

  • Conference paper
  • First Online:
Internet Science (INSCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10673))

Included in the following conference series:

Abstract

Large amounts of social media posts are produced on a daily basis and monitoring all of them is a challenging task. In this direction we demonstrate a topic detection and visualisation tool in Twitter data, which filters Twitter posts by topic or keyword, in two different languages; German and Turkish. The system is based on state-of-the-art news clustering methods and the tool has been created to handle streams of recent news information in a fast and user-friendly way. The user interface and user-system interaction examples are presented in detail.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    https://hootsuite.com/.

  2. 2.

    https://tweetreach.com/.

  3. 3.

    http://socialmention.com/.

  4. 4.

    http://new.twazzup.com/.

  5. 5.

    http://kristina-project.eu/en/.

  6. 6.

    https://www.multisensorproject.eu/.

  7. 7.

    https://dev.twitter.com/.

  8. 8.

    https://github.com/MKLab-ITI/topic-detection.

  9. 9.

    http://mklab-services.iti.gr/KRISTINA_topic_detection/.

  10. 10.

    https://github.com/telerik/kendo-ui-core.

  11. 11.

    http://edition.cnn.com/2017/05/15/world/macron-merkel-meeting/.

References

  1. Aggarwal, C.C., Zhai, C.: A survey of text clustering algorithms. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 77–128. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4614-3223-4_4

    Chapter  Google Scholar 

  2. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231. AAAI Press (1996)

    Google Scholar 

  3. Ankerst, M., Breunig, M.M., Kriegel, H., Sander, J.: OPTICS: ordering points to identify the clustering structure. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 49–60. ACM (1999)

    Google Scholar 

  4. Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS, vol. 7819, pp. 160–172. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_14

    Chapter  Google Scholar 

  5. Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Sharing clusters among related groups: Hierarchical Dirichlet processes. In: NIPS, pp. 1385–1392 (2004)

    Google Scholar 

  6. Kulis, B., Jordan, M.I.: Revisiting k-means: new algorithms via Bayesian nonparametrics. arXiv preprint arXiv:1111.0352 (2011)

  7. Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I.: A hybrid framework for news clustering based on the DBSCAN-martingale and LDA. Machine Learning and Data Mining in Pattern Recognition. LNCS, vol. 9729, pp. 170–184. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41920-6_13

    Chapter  Google Scholar 

  8. Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A., Charrad, M.M.: Package ‘NbClust’. J. Stat. Softw. 61, 1–36 (2014)

    Article  Google Scholar 

  9. Petkos, G., Schinas, M., Papadopoulos, S., Kompatsiaris, Y.: Graph-based multimodal clustering for social multimedia. Multimedia Tools Appl. 76, 7897–7919 (2017). https://doi.org/10.1007/s11042-016-3378-2

    Article  Google Scholar 

  10. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  11. Gialampoukidis, I., Liparas, D., Vrochidis, S., Kompatsiaris, I.: Query-based topic detection using concepts and named entities. In: Proceedings of the 1st International Workshop on Multimodal Media Data Analytics (MMDA 2016), pp. 9–13 (2016)

    Google Scholar 

  12. Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I., Antoniou, I.: Topic detection using the DBSCAN-Martingale and the time operator. In: 17th Conference of the Applied Stochastic Models and Data Analysis (ASMDA2017) (2017)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the EC-funded projects H2020-645012 (KRISTINA) and H2020-700475 (beAWARE).

Author information

Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stelios Andreadis, Ilias Gialampoukidis, Stefanos Vrochidis & Ioannis Kompatsiaris

Authors
  1. Stelios Andreadis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilias Gialampoukidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefanos Vrochidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ioannis Kompatsiaris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stelios Andreadis .

Editor information

Editors and Affiliations

  1. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. University of Warwick, Coventry, United Kingdom

    Jonathan Cave

  3. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Anna Satsiou

  4. Technical University Munich, Garching, Germany

    Georg Carle

  5. T6 Ecosystems, Rome, Italy

    Antonella Passani

  6. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Efstratios Kontopoulos

  7. CERTH, Information Technologies Institute, Thessaloniki, Greece

    Sotiris Diplaris

  8. Stockholm University, Stockholm, Sweden

    Donald McMillan

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Andreadis, S., Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I. (2017). A Topic Detection and Visualisation System on Social Media Posts. In: Kompatsiaris, I., et al. Internet Science. INSCI 2017. Lecture Notes in Computer Science(), vol 10673. Springer, Cham. https://doi.org/10.1007/978-3-319-70284-1_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70284-1_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70283-4

  • Online ISBN: 978-3-319-70284-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation