Advertisement

Detection of social events in streams of social multimedia

  • Jonathon HareEmail author
  • Sina Samangooei
  • Mahesan Niranjan
  • Nicholas Gibbins
Regular Paper

Abstract

Combining items from social media streams, such as Flickr photos and Twitter tweets, into meaningful groups can help users contextualise and consume more effectively the torrents of information continuously being made available on the social web. This task is made challenging due to the scale of the streams and the inherently multimodal nature of the information being contextualised. The problem of grouping social media items into meaningful groups can be seen as an ill-posed and application specific unsupervised clustering problem. A fundamental question in multimodal contexts is determining which features best signify that two items should belong to the same grouping. This paper presents a methodology which approaches social event detection as a streaming multi-modal clustering task. The methodology takes advantage of the temporal nature of social events and as a side benefit, allows for scaling to real-world datasets. Specific challenges of the social event detection task are addressed: the engineering and selection of the features used to compare items to one another; a feature fusion strategy that incorporates relative importance of features; the construction of a single sparse affinity matrix; and clustering techniques which produce meaningful item groups whilst scaling to cluster very large numbers of items. The state-of-the-art approach presented here is evaluated using the ReSEED dataset with standardised evaluation measures. With automatically learned feature weights, we achieve an \({F}_1\) score of 0.94, showing that a good compromise between precision and recall of clusters can be achieved. In a comparison with other state-of-the-art algorithms our approach is shown to give the best results.

Keywords

Social event detection Clustering methods Scalability User-generated content 

Notes

Acknowledgments

The authors would like to acknowledge the financial support of the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreements 270239 (ARCOMEM) and 287863 (TrendMiner). The effort of the 2013 MediaEval SED challenge organisers in creating the ReSEED social event dataset used in this paper is also acknowledged.

References

  1. 1.
    Becker H, Naaman M, Gravano L (2010) Learning similarity metrics for event identification in social media. In: Proceedings of the third ACM international conference on web search and data mining. ACM, New York, WSDM ’10, pp 291–300Google Scholar
  2. 2.
    Brenner M, Izquierdo E (2013) MediaEval 2013: social event detection, retrieval and classification in collaborative photo collections. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  3. 3.
    Chuang YY (2012) Affinity aggregation for spectral clustering. In: Proceedings of the 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Washington, CVPR ’12, pp 773–780. http://dl.acm.org/citation.cfm?id=2354409.2355074
  4. 4.
    Chung F (1997) Spectral graph theory, vol 92. American Mathematical Society, ProvidenceGoogle Scholar
  5. 5.
    De Vries CM, Geva S, Trotman A (2012) Document clustering evaluation: divergence from a random baseline, CoRRGoogle Scholar
  6. 6.
    Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. AAAI Press, KDDM, pp 226–231Google Scholar
  7. 7.
    Gupta I, Gautam K, Chandramouli K (2013) VIT@MediaEval 2013 social event detection task: semantic structuring of complementary information for clustering events. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  8. 8.
    Hare JS, Samangooei S, Dupplaw DP, Lewis PH (2013) Twitter’s visual pulse. In: ICMR’13. ACM, New York, pp 297–298Google Scholar
  9. 9.
    Larson M, Anguera X, Reuter T, Jones GJ, Ionescu B, Schedl M, Piatrik T, Hauff C, Soleymani M (eds) (2013) Working notes proceedings of the MediaEval 2013 workshopGoogle Scholar
  10. 10.
    Manchon-Vizuete D, Giro-I-Nieto X (2013) UPC at MediaEval 2013 social event detection task. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  11. 11.
    Mayurathan B, Pinidiyaarachchi U, Niranjan M (2013) Compact codebook design for visual scene recognition by sequential input space carving. In: MLSP, pp 1–6Google Scholar
  12. 12.
    Ng A, Jordan M, Weiss Y et al (2002) On spectral clustering: analysis and an algorithm. NIPS 2:849–856Google Scholar
  13. 13.
    Nguyen TVT, Dao MS, Mattivi R, Sansone E, Natale FGD, Boato G (2013) Event clustering and classification from social media: watershed-based and kernel methods. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  14. 14.
    Papaoikonomou A, Konstantinos Tserpes MK, Varvarigou T (2013) A similarity-based Chinese restaurant process for social event detection. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  15. 15.
    Petkos G, Papadopoulos S, Kompatsiaris Y (2012) Social event detection using multimodal clustering and integrating supervisory signals. In: Proc. ICMRGoogle Scholar
  16. 16.
    Rafailidis D, Semertzidis T, Lazaridis M, Strintzis MG, Daras P (2013) A data-driven approach for social event detection. In: [9]Google Scholar
  17. 17.
    Reuter T, Cimiano P (2012) Event-based classification of social media streams. In: Proc, ICMRGoogle Scholar
  18. 18.
    Reuter T, Papadopoulos S, Mezaris V, Cimiano P, de Vries C, Geva S (2013) Social event detection at MediaEval 2013: challenges, datasets, and evaluation. In: MediaEval 2013 workshopGoogle Scholar
  19. 19.
    Reuter T, Papadopoulos S, Mezaris V, Cimiano P (2014) Reseed: social event detection dataset. In: Proceedings of the 5th ACM multimedia systems conference. ACM, New York, MMSys ’14, pp 35–40. doi: 10.1145/2557642.2563674
  20. 20.
    Risse T, Peters W (2012) Arcomem: From collect-all archives to community memories. In: Proceedings of the 21st international conference companion on world wide web. ACM, New York, WWW ’12 Companion, pp 275–278. doi: 10.1145/2187980.2188027
  21. 21.
    Samangooei S, Hare J, Dupplaw D, Niranjan M, Gibbins N, Lewis P, Davies J, Jain N, Preston J (2013) Social event detection via sparse multi-modal feature selection and incremental density based clustering. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  22. 22.
    Scherp A, Jain R, Kankanhalli M, Mezaris V (2010) Modeling, detecting, and processing events in multimedia. In: Proceedings of the international conference on multimedia. ACM, New York, MM ’10, pp 1739–1740Google Scholar
  23. 23.
    Schinas M, Mantziou E, Papadopoulos S, Petkos G, Kompatsiaris Y (2013) CERTH @ MediaEval 2013 social event detection task. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  24. 24.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. PAMI 22(8):888–905CrossRefGoogle Scholar
  25. 25.
    Smyth S, White S (2005) A spectral clustering approach to finding communities in graphs. In: Proceedings of the 5th SIAM international conference on data mining, pp 76–84Google Scholar
  26. 26.
    Sutanto T, Nayak R (2013) ADMRG @ MediaEval 2013 social event detection. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  27. 27.
    Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416MathSciNetCrossRefGoogle Scholar
  28. 28.
    Wistuba M, Schmidt-Thieme L (2013) Supervised clustering of social media streams. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar
  29. 29.
    Xu X, Yuruk N, Feng Z, Schweiger TAJ (2007) Scan: a structural clustering algorithm for networks. In: Proc. SIGKDD. ACM, New York, KDD ’07, pp 824–833Google Scholar
  30. 30.
    Zaharieva M, Zeppelzauer M, Breiteneder C (2013) Automated social event detection in large photo collections. In: Proceedings of the 3rd ACM conference on international conference on multimedia retrieval. ACM, New York, ICMR’13, pp 167–174Google Scholar
  31. 31.
    Zandbergen PA, Barbeau SJ (2011) Positional accuracy of assisted gps data from high-sensitivity gps-enabled mobile phones. J Navig 64:381–399. doi: 10.1017/S0373463311000051
  32. 32.
    Zeppelzauer M, Zaharieva M, Fabro MD (2013) Unsupervised clustering of social events. In: Working notes proceedings of the MediaEval 2013 workshop, CEUR-WS.org, Barcelona, Spain, 18–19 October 2013Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Jonathon Hare
    • 1
    Email author
  • Sina Samangooei
    • 2
  • Mahesan Niranjan
    • 1
  • Nicholas Gibbins
    • 1
  1. 1.University of SouthamptonSouthamptonUK
  2. 2.AmazonCambridgeUK

Personalised recommendations