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Solving Large Scale Learning Tasks. Challenges and Algorithms pp 42–84Cite as

Detecting Events in Online Social Networks: Definitions, Trends and Challenges

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9580))

Abstract

Event detection is a research area that attracted attention during the last years due to the widespread availability of social media data. The problem of event detection has been examined in multiple social media sources like Twitter, Flickr, YouTube and Facebook. The task comprises many challenges including the processing of large volumes of data and high levels of noise. In this article, we present a wide range of event detection algorithms, architectures and evaluation methodologies. In addition, we extensively discuss on available datasets, potential applications and open research issues. The main objective is to provide a compact representation of the recent developments in the field and aid the reader in understanding the main challenges tackled so far as well as identifying interesting future research directions.

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Notes

  1. 1.

    http://www.itl.nist.gov/iad/mig/tests/tdt/resources.html.

  2. 2.

    http://www.itl.nist.gov/iad/mig/tests/tdt/tasks/fsd.html.

  3. 3.

    http://www.itl.nist.gov/iad/mig//tests/tdt/1998/.

  4. 4.

    http://research.microsoft.com/en-us/collaboration/focus/cs/web-ngram.aspx.

  5. 5.

    https://foursquare.com/.

  6. 6.

    https://dev.twitter.com/docs/api/1.1/get/search/tweets.

  7. 7.

    http://mpqa.cs.pitt.edu/opinionfinder/.

  8. 8.

    https://foursquare.com/.

  9. 9.

    http://www.multimediaeval.org/mediaeval2012/.

  10. 10.

    https://cloud.google.com/translate/docs.

  11. 11.

    http://meta.wikimedia.org/wiki/Data_dumps#Content.

  12. 12.

    http://dbpedia.org/About.

  13. 13.

    http://www.google.com/trends/.

  14. 14.

    http://storm.incubator.apache.org/.

  15. 15.

    https://www.mongodb.org/.

  16. 16.

    https://josm.openstreetmap.de/.

  17. 17.

    https://lucene.apache.org/solr/.

  18. 18.

    https://storm.apache.org/.

  19. 19.

    http://www.insight-ict.eu/.

  20. 20.

    https://dev.twitter.com/streaming/reference/post/statuses/filter.

  21. 21.

    https://www.openstreetmap.org.

  22. 22.

    https://lucene.apache.org/.

  23. 23.

    Available at http://demeter.inf.ed.ac.uk/cross/docs/fsd_corpus.tar.gz.

  24. 24.

    Available at http://demeter.inf.ed.ac.uk/cross/docs/Newswire_Events.tar.gz.

  25. 25.

    http://en.wikipedia.org/wiki/Sydney_Coordinated_Adaptive_Traffic_System.

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Acknowledgments

This work is funded by the projects EU FP7 INSIGHT (318225), GGET Thalis DISFER and GeomComp.

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  1. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimioupolis, Ilisia, 15784, Athens, Greece

    Nikolaos Panagiotou, Ioannis Katakis & Dimitrios Gunopulos

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  1. Nikolaos Panagiotou
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Correspondence to Ioannis Katakis .

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  1. TU Dortmund , Dortmund, Germany

    Stefan Michaelis

  2. TU Dortmund , Dortmund, Germany

    Nico Piatkowski

  3. TU Dortmund , Dortmund, Germany

    Marco Stolpe

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Panagiotou, N., Katakis, I., Gunopulos, D. (2016). Detecting Events in Online Social Networks: Definitions, Trends and Challenges. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-41706-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41705-9

  • Online ISBN: 978-3-319-41706-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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