Skip to main content
Springer Nature Link
Log in

Online Social Networks Event Detection: A Survey

  • Chapter
  • First Online:
Solving Large Scale Learning Tasks. Challenges and Algorithms

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

Abstract

Today online social network services are challenging state-of-the-art social media mining algorithms and techniques due to its real-time nature, scale and amount of unstructured data generated. The continuous interactions between online social network participants generate streams of unbounded text content and evolutionary network structures within the social streams that make classical text mining and network analysis techniques obsolete and not suitable to deal with such new challenges. Performing event detection on online social networks is no exception, state-of-the-art algorithms rely on text mining techniques applied to pre-known datasets that are being processed with no restrictions on the computational complexity and required execution time per document analysis. Moreover, network analysis algorithms used to extract knowledge from users relations and interactions were not designed to handle evolutionary networks of such order of magnitude in terms of the number of nodes and edges. This specific problem of event detection becomes even more serious due to the real-time nature of online social networks. New or unforeseen events need to be identified and tracked on a real-time basis providing accurate results as quick as possible. It makes no sense to have an algorithm that provides detected event results a few hours after being announced by traditional newswire.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://en.wikipedia.org/wiki/F1_score.

References

  1. Agarwal, M.K., Ramamritham, K., Bhide, M.: Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. Proc. VLDB Endow. 5(10), 980–991 (2012). http://arxiv.org/abs/1207.0138

    Google Scholar 

  2. Aggarwal, C.C., Zhai, C.: A survey of text clustering algorithms. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 77–128. Springer, New York (2012)

    Chapter  Google Scholar 

  3. Allan, J.: Topic Detection and Tracking: Event-based Information Organization. The Kluwer International Series on Information Retrieval, vol. 12. Springer, New York (2002). http://portal.acm.org/citation.cfm?id=772260

    MATH  Google Scholar 

  4. Allan, J., Jin, H., Rajman, M., Wayne, C., Gildea, D., Lavrenko, V., Hoberman, R., Caputo, D.: Topic-based novelty detection 1999 summer workshop at CLSP final report (1999). http://old-site.clsp.jhu.edu/ws99/projects/tdt/final_report/report.pdf. Accessed 2 Nov 2013

  5. Allan, J., Lavrenko, V., Jin, H.: First story detection in TDT is hard. In: CIKM 2000 Proceedings of the Ninth International Conference on Information and Knowledge Management, pp. 374–381. ACM (2000)

    Google Scholar 

  6. Allan, J., Lavrenko, V., Malin, D., Swan, R.: Detections, Bounds, and Timelines: UMass and TDT-3. Information Retrieval, pp. 167–174 (2000). http://maroo.cs.umass.edu/pdf/IR-201.pdf

  7. Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 1998, New York, USA, pp. 37–45 (1998). http://portal.acm.org/citation.cfm?doid=290941.290954

  8. Angel, A., Koudas, N., Sarkas, N., Srivastava, D.: What’s on the grapevine? In: Proceedings of the 35th SIGMOD International Conference on Management of Data - SIGMOD 2009, p. 1047 (2009). http://portal.acm.org/citation.cfm?doid=1559845.1559977

  9. Atefeh, F., Khreich, W.: A survey of techniques for event detection in Twitter. Comput. Intell. (2013). http://doi.wiley.com/10.1111/coin.12017

  10. Bampis, E., Jansen, K., Kenyon, C.: Efficient Approximation and Online Algorithms. Springer, Heidelberg (2010). http://www.amazon.com/Efficient-Approximation-Online-Algorithms-Combinatorial/dp/3540322124

    Book  MATH  Google Scholar 

  11. Barabasi, A.L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207 (2005). http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0505371

    Article  Google Scholar 

  12. Becker, H., Chen, F., Iter, D.: Automatic identification and presentation of Twitter content for planned events. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp. 655–656 (2011). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/download/2743/3198

  13. Becker, H., Iter, D., Naaman, M., Gravano, L.: Identifying content for planned events across social media sites. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining - WSDM 2012, p. 533 (2012). http://dl.acm.org/citation.cfm?doid=2124295.2124360

  14. Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: ICWSM, pp. 438–441. Technical Report CUCS-012-11, Columbia University. The AAAI Press (2011). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewPDFInterstitial/2745/3207

  15. Benson, E., Haghighi, A., Barzilay, R.: Event discovery in social media feeds. Artif. Intell. 3(2–3), 389–398 (2011). http://aria42.com/pubs/events.pdf, http://dl.acm.org/citation.cfm?id=2002472.2002522

    Google Scholar 

  16. Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006). http://link.springer.com/chapter/10.1007/3-540-28349-8_2

    Chapter  Google Scholar 

  17. Bifet, A., Kirkby, R.: Data stream mining: a practical approach. Technical report, The University of Waikato, August 2009

    Google Scholar 

  18. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003). http://www.crossref.org/jmlr_DOI.html

    MATH  Google Scholar 

  19. Brants, T., Chen, F.: A system for new event detection. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval SIGIR 2003, 2002, p. 330 (2003). http://portal.acm.org/citation.cfm?doid=860435.860495

  20. Cataldi, M., Torino, U., Caro, L.D., Schifanella, C.: Emerging topic detection on Twitter based on temporal and social terms evaluation. In: Proceedings of the Tenth International Workshop onMultimedia Data Mining, pp. 1–10 (2010). http://dl.acm.org/citation.cfm?id=1814245.1814249

  21. Chen, C.C., Chen, Y.-T., Sun, Y., Chen, M.-C.: Life cycle modeling of news events using aging theory. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 47–59. Springer, Heidelberg (2003). http://link.springer.com/chapter/10.1007/978-3-540-39857-8_7

    Chapter  Google Scholar 

  22. Chen, L., Roy, A.: Event detection from flickr data through wavelet-based spatial analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 523–532 (2009). http://dl.acm.org/citation.cfm?id=1646021npapers2://publication/uuid/8EC6E15D-D958-4A0A-88E5-8D62631BF7C5

  23. Cordeiro, M.: Twitter event detection: combining wavelet analysis and topic inference summarization. In: The Doctoral Symposium on Informatics Engineering - DSIE 2012 (2012). http://paginas.fe.up.pt/~prodei/dsie12/papers/paper_14.pdf

  24. Corley, C.D., Dowling, C., Rose, S.J., McKenzie, T.: SociAL sensor analytics: measuring phenomenology at scale. In: 2013 IEEE International Conference on Intelligence and Security Informatics, pp. 61–66. IEEE, June 2013. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6578787

  25. Dou, W., Wang, X., Skau, D., Ribarsky, W., Zhou, M.X.: LeadLine: interactive visual analysis of text data through event identification and exploration. In: IEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings, pp. 93–102 (2012)

    Google Scholar 

  26. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. October 2000. http://dl.acm.org/citation.cfm?id=954544

  27. Erosheva, E., Fienberg, S., Lafferty, J.: Mixed-membership models of scientific publications. Proc. Natl. Acad. Sci. U.S.A. 101(Suppl 1), 5220–5227 (2004)

    Article  Google Scholar 

  28. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.2930

  29. Farzindar, A.: Social network integration in document summarization. In: Fiori, A. (ed.) Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding. IGI-Global, Hershey (2014)

    Google Scholar 

  30. Fiscus, J.G., Doddington, G.R.: Topic detection and tracking evaluation overview. In: Topic Detection and Tracking, pp. 17–31 (2002). http://www.springerlink.com/index/T652P42711XW6421.pdf

    Google Scholar 

  31. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.9093

    Article  MathSciNet  MATH  Google Scholar 

  32. Fung, G.G.P.C., Yu, J.X.J., Yu, P.P.S., Lu, H.: Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases - VLDB 2005, vol. 1, pp. 181–192 (2005). http://dl.acm.org/citation.cfm?id=1083616 http://www.scopus.com/inward/record.url?eid=2-s2.0-33745624002&partnerID=tZOtx3y1

  33. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: VLDB 1999: Proceedings of the 25th International Conference on Very Large Data Bases, pp. 518–529 (1999). http://portal.acm.org/citation.cfm?id=671516

  34. Goorha, S., Ungar, L.: Discovery of significant emerging trends. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), p. 57 (2010). http://dl.acm.org/citation.cfm?doid=1835804.1835815

  35. Gu, H., Xie, X., Lv, Q., Ruan, Y., Shang, L.: ETree: effective and efficient event modeling for real-time online social media networks. In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 300–307. IEEE, August 2011. http://dl.acm.org/citation.cfm?id=2052138.2052366

  36. He, Q., Chang, K., Lim, E.P.: Analyzing feature trajectories for event detection. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 2007, p. 207 (2007). http://portal.acm.org/citation.cfm?doid=1277741.1277779\(\backslash \)n, http://doi.acm.org/10.1145/1277741.1277779

  37. He, Q., Chang, K., Lim, E., Zhang, J.: Bursty feature representation for clustering text streams. In: SDM, pp. 491–496 (2007). https://www.siam.org/proceedings/datamining/2007/dm07_050he.pdf

  38. Hounshell, B.: The revolution will be tweeted. Foreign Policy 187, 20–21 (2011)

    Google Scholar 

  39. Hu, M., Sun, A., Lim, E.P.: Event detection with common user interests. In: Proceeding of the 10th ACM Workshop on Web Information and Data Management - WIDM 2008, New York, USA, p. 1. ACM, New York, October 2008. http://dl.acm.org/citation.cfm?id=1458502.1458504

  40. Hussein, D., Alaa, G., Hamad, A.: Towards usage-centered design patterns for social networking systems. In: Park, J.J., Yang, L.T., Lee, C. (eds.) FutureTech 2011, Part II. CCIS, vol. 185, pp. 80–89. Springer, Heidelberg (2011). http://dx.doi.org/10.1007/978-3-642-22309-9_10

    Chapter  Google Scholar 

  41. Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey, July 2014. http://arxiv.org/abs/1407.7071

  42. Jo, T., Lee, M.R.: The evaluation measure of text clustering for the variable number of clusters. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part II. LNCS, vol. 4492, pp. 871–879. Springer, Heidelberg (2007). http://dx.doi.org/10.1007/978-3-540-72393-6_104

    Chapter  Google Scholar 

  43. Jurgens, D., Stevens, K.: Event detection in blogs using temporal random indexing. In: Proceedings of the Workshop on Events in Emerging Text Types, pp. 9–16 (2009). http://dl.acm.org/citation.cfm?id=1859650.1859652

  44. Kanerva, P., Kristofersson, J., Holst, A.: Random indexing of text samples for latent semantic analysis. In: Proceedings of the 22nd Annual Conference of the Cognitive Science Society, vol. 1036, pp. 16429–16429 (2000). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.6523&rep=rep1&type=pdf

  45. Keogh, E.: Exact indexing of dynamic time warping. In: Proceedings of the 28th International Conference on Very Large Data Bases VLDB 2002, pp. 406–417, August 2002. http://dl.acm.org/citation.cfm?id=1287369.1287405

    Google Scholar 

  46. Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2002, vol. 7, no. 4, p. 91 (2002). http://portal.acm.org/citation.cfm?doid=775047.775061

  47. Kohonen, T.: The self-organizing map. Proc. IEEE 78, 1464–1480 (1990)

    Article  Google Scholar 

  48. Kontostathis, A., Galitsky, L.M., Pottenger, W.M., Roy, S., Phelps, D.J.: A survey of emerging trend detection in textual data mining. In: Berry, M.W. (ed.) Survey of Text Mining. Springer, New York (2004). http://link.springer.com/chapter/10.1007/978-1-4757-4305-0_9

    Google Scholar 

  49. Kumaran, G., Allan, J.: Text classification and named entities for new event detection. In: Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 297–304 (2004). http://portal.acm.org/citation.cfm?doid=1008992.1009044

  50. Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? Categories and subject descriptors. Most 112(2), 591–600 (2010). http://portal.acm.org/citation.cfm?doid=1772690.1772751

    Google Scholar 

  51. Lardinois, F.: Readwritesocial: the short lifespan of a tweet: retweets only happen within the first hour (2010). http://readwrite.com/2010/09/29/the_short_lifespan_of_a_tweet_retweets_only_happen. Accessed 2 Apr 2013

  52. Lee, R., Sumiya, K.: Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, pp. 1–10 (2010). http://doi.acm.org/10.1145/1867699.1867701

  53. Leek, T., Schwartz, R., Sista, S.: Probabilistic approaches to topic detection and tracking. In: Topic Detection and Tracking, pp. 67–83 (2002). http://portal.acm.org/citation.cfm?id=772260.772265

    Google Scholar 

  54. Lemire, D.: A better alternative to piecewise linear time series segmentation. In: SIAM Data Mining 2007 (2007)

    Google Scholar 

  55. Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management - CIKM 2012, New York, USA, p. 155. ACM, New York, October 2012. http://dl.acm.org/citation.cfm?id=2396761.2396785

  56. Li, R., Lei, K.H., Khadiwala, R., Chang, K.C.C.: TEDAS: a Twitter-based event detection and analysis system. In: Kementsietsidis, A., Salles, M.A.V. (eds.) 2012 IEEE 28th International Conference on Data Engineering, pp. 1273–1276. IEEE, April 2012. http://dblp.uni-trier.de/db/conf/icde/icde2012.html#LiLKC12, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6228186

  57. Linguistic Data Consortium: TDT 2004: Annotation Manual - version 1.2, 4 August 2004. http://projects.ldc.upenn.edu/TDT5/Annotation/TDT2004V1.2.pdf

  58. Long, R., Wang, H., Chen, Y., Jin, O., Yu, Y.: Towards effective event detection, tracking and summarization on microblog data. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds.) WAIM 2011. LNCS, vol. 6897, pp. 652–663. Springer, Heidelberg (2011). http://dl.acm.org/citation.cfm?id=2035562.2035636

    Chapter  Google Scholar 

  59. Makkonen, J., Ahonen-Myka, H., Salmenkivi, M.: Topic detection and tracking with spatio-temporal evidence. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 251–265. Springer, Heidelberg (2003). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.8469

    Chapter  Google Scholar 

  60. Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=868688

    Article  Google Scholar 

  61. Massoudi, K., Tsagkias, M., de Rijke, M., Weerkamp, W.: Incorporating query expansion and quality indicators in searching microblog posts, pp. 362–367, April 2011. http://dl.acm.org/citation.cfm?id=1996889.1996936

    Google Scholar 

  62. Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the 2010 International Conference on Management of Data - SIGMOD 2010, p. 1155. ACM, New York (2010). http://portal.acm.org/citation.cfm?id=1807306, http://portal.acm.org/citation.cfm?doid=1807167.1807306

  63. Metzler, D., Cai, C., Hovy, E.: Structured event retrieval over microblog archives. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 646–655 (2012). http://www.aclweb.org/anthology/N12-1083

  64. Mohd, M.: Named entity patterns across news domains. In: BCS IRSG Symposium: Future Directions in Information Access (FDIA), pp. 1–6 (2007). http://www.mendeley.com/research/named-entity-patterns-across-news-domains/

  65. Muthukrishnan, S.: Data streams: algorithms and applications. Found. Trends Theoret. Comput. Sci. 1(2), 117–236 (2005). http://www.nowpublishers.com/product.aspx?product=TCS&doi=0400000002

  66. Osborne, M., Lavrenko, V., Petrovic, S., Osborne, M., Lavrenko, V.: Using paraphrases for improving first story detection in news and Twitter. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies, pp. 338–346. The Association for Computational Linguistics (2012). http://www.aclweb.org/anthology/N12-1034

  67. Osborne, M., Petrovic, S., McCreadie, R., Macdonald, C., Ounis, I.: Bieber no more: first story detection using Twitter and Wikipedia. In: Proceedings of TAIA 2012 (2012)

    Google Scholar 

  68. Ozdikis, O., Senkul, P., Oguztuzun, H.: Semantic expansion of hashtags for enhanced event detection in Twitter. In: The First International Workshop on Online Social Systems (WOSS 2012) (2012)

    Google Scholar 

  69. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. World Wide Web Internet Web Inf. Syst. 54, 1–17 (1998). http://ilpubs.stanford.edu:8090/422

    Google Scholar 

  70. Pan, C.C., Mitra, P.: Event detection with spatial latent dirichlet allocation. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, vol. 20, pp. 349–358 (2011). http://portal.acm.org/citation.cfm?id=1315460

  71. Paranjpe, D.: Learning document aboutness from implicit user feedback and document structure. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, p. 365 (2009). http://dl.acm.org/citation.cfm?id=1645953.1646002

  72. Petrovic, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: Proceedings of NAACL (2010). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.170.9438&rep=rep1&type=pdf

  73. Petrovic, S.: Real-time event detection in massive streams. Ph.D. thesis, University of Edinburgh (2012). http://homepages.inf.ed.ac.uk/s0894589/petrovic-thesis.pdf

  74. Petrovic, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: HLT-NAACL, pp. 181–189. The Association for Computational Linguistics (2010)

    Google Scholar 

  75. Petrovic, S., Osborne, M., McCreadie, R., Macdonald, C., Ounis, I., Shrimpton, L.: Can Twitter replace newswire for breaking news? In: 7th International AAAI Conference on Web and Social Media (ICWSM) (2013)

    Google Scholar 

  76. Phuvipadawat, S., Murata, T.: Breaking news detection and tracking in Twitter. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2010, vol. 3, pp. 120–123. IEEE, August 2010. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5616930

  77. Li, P., Burges, C.J.C., Wu, Q.: McRank: learning to rank using multiple classification and gradient boosting. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 3–6 December 2007. http://www.researchgate.net/publication/221619438_McRank_Learning_to_Rank_Using_Multiple_Classification_and_Gradient_Boosting, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.6630

  78. Pohl, D., Bouchachia, A., Hellwagner, H.: Automatic identification of crisis-related sub-events using clustering. In: Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, vol. 2, pp. 333–338 (2012)

    Google Scholar 

  79. Popescu, A.M., Pennacchiotti, M.: Detecting controversial events from Twitter. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management - CIKM 2010, New York, USA, p. 1873 (2010). http://dl.acm.org/citation.cfm?id=1871751, http://portal.acm.org/citation.cfm?doid=1871437.1871751

  80. Popescu, A.M., Pennacchiotti, M., Paranjpe, D.: Extracting events and event descriptions from Twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web - WWW 2011, New York, USA, p. 105. ACM, New York, March 2011. http://dl.acm.org/citation.cfm?id=1963192.1963246

  81. Raimond, Y., Abdallah, S.: The event ontology (2007). http://motools.sf.net/event

  82. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2012). http://www.amazon.de/Mining-Massive-Datasets-Anand-Rajaraman/dp/1107015359/ref=sr_1_1?ie=UTF8&qid=1350890245&sr=8-1

  83. Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, 103 pages (2007)

    Google Scholar 

  84. Ritter, A., Clark, S., Mausam, Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534 (2011). http://www.aclweb.org/anthology/D11-1141

  85. Ritter, A., Etzioni, O., Clark, S.: Open domain event extraction from Twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2012, p. 1104 (2012). http://dl.acm.org/citation.cfm?id=2339530.2339704

  86. Robinson, B., Power, R., Cameron, M.: A sensitive Twitter earthquake detector. In: WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web, pp. 999–1002 (2013). http://www.scopus.com/inward/record.url?eid=2-s2.0-84893039051&partnerID=tZOtx3y1

  87. Sahlgren, M.: Vector-based semantic analysis: representing word meaning based on random labels. In: ESSLI Workshop on Semantic Knowledge Acquistion and Categorization (2002). http://www.sics.se/~mange/papers/VBSA_Esslli.ps

  88. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010). http://dl.acm.org/citation.cfm?id=1772690.1772777

  89. Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: TwitterStand: news in tweets. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS 2009, New York, USA, vol. 156, p. 42 (2009). http://portal.acm.org/citation.cfm?id=1653781, http://portal.acm.org/citation.cfm?doid=1653771.1653781

  90. Sayyadi, H., Hurst, M., Maykov, A., Livelabs, M.: Event detection and tracking in social streams. In: Proceedings of International Conference on Weblogs and Social Media (ICWSM), pp. 311–314 (2009). http://www.aaai.org/ocs/index.php/ICWSM/09/paper/viewFile/170/493

  91. Schonfeld, E.: Techcrunch: mining the thought stream (2009). http://techcrunch.com/2009/02/15/mining-the-thought-stream. Accessed 9 July 2013

  92. Snowsill, T., Nicart, F., Stefani, M., De Bie, T., Cristianini, N.: Finding surprising patterns in textual data streams. In: 2010 2nd International Workshop on Cognitive Information Processing, pp. 405–410, June 2010. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5604085

  93. Strassel, S.: Topic Detection & Traking (TDT-5) (2004). http://www.ldc.upenn.edu/Projects/TDT2004

  94. Tanev, H., Ehrmann, M., Piskorski, J., Zavarella, V.: Enhancing event descriptions through Twitter mining. In: Sixth International AAAI Conference on Weblogs and Social Media, pp. 587–590 (2012). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4631\(\backslash \)n, http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4631/5065

  95. Baldwin, T., Paul Cook, M., Baldwin, T., Cook, P., Lui, M., Mackinlay, A., Wang, L.: How noisy social media text, how diffrnt social media sources? In: Proceedings of IJCNLP 2013, pp. 356–364 (2013). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.1683

  96. Wang, X., Grimson, E.: Spatial latent Dirichlet allocation. In: Advances in Neural Information Processing Systems, vol. 20, pp. 1–8 (2007). http://people.csail.mit.edu/xgwang/papers/STLDA.pdf

  97. Wang, X., Zhai, C., Hu, X., Sproat, R.: Mining correlated burstytopic patterns from coordinated text streams. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 784–793 (2007). http://dl.acm.org/citation.cfm?id=1281276\(\backslash \)npapers2://publication/uuid/A6A05DF5-1873-4DC4-BAB3-F73712691FCA

  98. Weng, J., Yao, Y., Leonardi, E., Lee, F.: Event detection in Twitter. Development 98, 401–408 (2011). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/download/2767/3299

    Google Scholar 

  99. Wikipedia: Facebook — Wikipedia, the free encyclopedia (2013). http://en.wikipedia.org/w/index.php?title=Facebook&oldid=548760277. Accessed 7 Apr 2013

  100. Wikipedia: Google+ — Wikipedia, the free encyclopedia (2013). http://en.wikipedia.org/w/index.php?title=Google%2B&oldid=548920007. Accessed 7 Apr 2013

  101. Wikipedia: Linkedin — Wikipedia, the free encyclopedia (2013). http://en.wikipedia.org/w/index.php?title=LinkedIn&oldid=549175950. Accessed 7 Apr 2013

  102. Wikipedia: Twitter — Wikipedia, the free encyclopedia (2013). http://en.wikipedia.org/w/index.php?title=Twitter&oldid=549164139. Accessed 7 Apr 2013

  103. Yang, C.C., Shi, X., Wei, C.P.: Discovering event evolution graphs from news corpora. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39, 850–863 (2009)

    Article  Google Scholar 

  104. Yang, Y., Carbonell, J., Brown, R., Pierce, T., Archibald, B., Liu, X.: Learning approaches for detecting and tracking news events (1999)

    Google Scholar 

  105. Yang, Y., Pierce, T.T., Carbonell, J.G.: A study of retrospective and on-line event detection. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, 24–28 August 1998, pp. 28–36. ACM, New York (1998). http://portal.acm.org/citation.cfm?doid=290941.290953

  106. Yang, Y., Zhang, J., Carbonell, J., Jin, C.: Topic-conditioned novelty detection. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2002, pp. 688–693 (2002). http://dl.acm.org/citation.cfm?id=775047.775150

  107. Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, 2009, vol. 4, p. 937 (2009). http://portal.acm.org/citation.cfm?doid=1557019.1557121

  108. Zhao, Q., Chen, B., Mitra, P.: Temporal and information flow based event detection from social text streams. In: Proceedings of the 22nd National Conference on Artificial Intelligence - AAAI 2007, vol. 2, pp. 1501–1506. AAAI Press (2007). http://www.aaai.org/Papers/AAAI/2007/AAAI07-238.pdf

Download references

Acknowledgements

This work was supported by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT), and by European Commission through the project MAESTRA (Grant number ICT-2013-612944).

Author information

Authors and Affiliations

  1. University of Porto, Porto, Portugal

    Mário Cordeiro

  2. INESC TEC Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

Authors
  1. Mário Cordeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Gama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mário Cordeiro .

Editor information

Editors and Affiliations

  1. TU Dortmund , Dortmund, Germany

    Stefan Michaelis

  2. TU Dortmund , Dortmund, Germany

    Nico Piatkowski

  3. TU Dortmund , Dortmund, Germany

    Marco Stolpe

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Cordeiro, M., Gama, J. (2016). Online Social Networks Event Detection: A Survey. 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_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41706-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41705-9

  • Online ISBN: 978-3-319-41706-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics