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Erschienen in:
Online Social Networks Event Detection: A Survey Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

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

verfasst von : Nikolaos Panagiotou, Ioannis Katakis, Dimitrios Gunopulos

Erschienen in: Solving Large Scale Learning Tasks. Challenges and Algorithms

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Online Social Networks Event Detection: A Survey
Nächstes Kapitel Why Do We Need Data Privacy?
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Literatur
1.
Abdelhaq, H., Sengstock, C., Gertz, M.: EvenTweet: online localized event detection from twitter. Proc. VLDB Endow. 6(12), 1326–1329 (2013)CrossRef
2.
Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: SDM, pp. 624–635. SIAM/Omnipress (2012)
3.
Allan, J.: Introduction to topic detection and tracking. In: Allan, J. (ed.) Topic Detection and Tracking, pp. 1–16. Springer, New York (2002)CrossRef
4.
Allan, J., Lavrenko, V., Malin, D., Swan, R.: Detections, bounds, and timelines: Umass and TDT-3. In: Proceedings of Topic Detection and Tracking Workshop, pp. 167–174 (2000)
5.
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. ACM (1998)
6.
Ankerst, M., Breunig, M., Kriegel, H., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28, 49–60 (1999)CrossRef
7.
8.
Artikis, A., Weidlich, M., Schnitzler, F., Boutsis, I., Liebig, T., Piatkowski, N., Bockermann, C., Morik, K., Kalogeraki, V., Marecek, J., et al.: Heterogeneous stream processing and crowdsourcing for urban traffic management. In: EDBT, pp. 712–723 (2014)
9.
Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Computat. Intell. 31, 132–164 (2013)MathSciNetCrossRef
10.
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519–528. ACM (2012)
11.
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)
12.
Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2011), pp. 1–17 (2011)
13.
14.
Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 1–15. Springer, Heidelberg (2010)CrossRef
15.
Boettcher, A., Lee, D.: EventRadar: a real-time local event detection scheme using twitter stream. In: 2012 IEEE International Conference on Green Computing and Communications, pp. 358–367, November 2012
16.
Boutsis, I., Kalogeraki, V.: Privacy preservation for participatory sensing data. In: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 103–113. IEEE (2013)
17.
Boutsis, I., Kalogeraki, V., Gunopulos, D.: Efficient event detection by exploiting crowds. In: Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems, pp. 123–134. ACM (2013)
18.
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: the million follower fallacy. In: ICWSM 2010, pp. 10–17 (2010)
19.
Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. Theort. Comput. Sci. 312(1), 3–15 (2004)MathSciNetCrossRefMATH
20.
Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 661–670. ACM (2012)
21.
Chowdhury, G.: Introduction to Modern Information Retrieval. Facet Publishing, London (2010)
22.
23.
24.
25.
26.
Daly, E.M., Geyer, W.: Effective event discovery: using location and social information for scoping event recommendations. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 277–280. ACM (2011)
27.
Daly, E.M., Lecue, F., Bicer, V.: Westland row why so slow?: fusing social media and linked data sources for understanding real-time traffic conditions. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 203–212. ACM (2013)
28.
De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: AAAI Conference on Weblogs and Social Media, vol. 2 (2013)
29.
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)MATH
30.
Fuchs, G., Andrienko, N., Andrienko, G., Bothe, S., Stange, H.: Tracing the German centennial flood in the stream of tweets: first lessons learned. In: Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, GEOCROWD 2013, pp. 31–38. ACM, New York (2013). http://​doi.​acm.​org/​10.​1145/​2534732.​2534741
31.
Fung, G.P.C., Yu, J.X., Yu, P.S., Lu, H.: Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 181–192. VLDB Endowment (2005)
32.
33.
Ghosh, S., Sharma, N., Benevenuto, F., Ganguly, N., Gummadi, K.: Cognos: crowdsourcing search for topic experts in microblogs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 575–590. ACM (2012)
34.
Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012–1014 (2009)CrossRef
35.
Go, A., Huang, L., Bhayani, R.: Twitter sentiment analysis. Nature 17, 1–6 (2009)
36.
Goot, E.V.D., Tanev, H., Linge, J.: Combining twitter and media reports on public health events in medisys. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 703–705. International World Wide Web Conferences Steering Committee (2013). http://​dl.​acm.​org/​citation.​cfm?​id=​2488028
37.
Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web, pp. 491–501. ACM (2004)
38.
Guo, D., Wu, J., Chen, H., Yuan, Y., Luo, X.: The dynamic bloom filters. IEEE Trans. Knowl. Data Eng. 22(1), 120–133 (2010)CrossRef
39.
40.
Itti, L., Baldi, P.: A principled approach to detecting surprising events in video. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 631–637. IEEE (2005)
41.
Jarvis, R.A., Patrick, E.A.: Clustering using a similarity measure based on shared near neighbors. IEEE Trans. Comput. 100(11), 1025–1034 (1973)CrossRef
42.
Kaleel, S.B.: Event Detection and trending in multiple social networking sites. In: Proceedings of the 16th Communications and Networking Symposium. Society for Computer Simulation International (2013)
43.
Karkali, M., Rousseau, F., Ntoulas, A., Vazirgiannis, M.: Efficient online novelty detection in news streams. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds.) WISE 2013, Part I. LNCS, vol. 8180, pp. 57–71. Springer, Heidelberg (2013)CrossRef
44.
45.
Knobel, M., Lankshear, C.: Online memes, affinities, and cultural production. In: Knobel, M., Lankshear, C. (eds.) A New Literacies Sampler, pp. 199–227. Peter Lang, New York (2007)
46.
Kotsakos, D., Sakkos, P., Katakis, I., Gunopulos, D.: # tag: meme or event? In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 391–394. IEEE (2014)
47.
Kumar, S., Liu, H., Mehta, S., Subramaniam, L.V.: From tweets to events: exploring a scalable solution for twitter streams. arXiv preprint, arXiv:​1405.​1392 (2014)
48.
Kumaran, G., Allan, J.: Using names and topics for new event detection. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 121–128. Association for Computational Linguistics (2005)
49.
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM (2010)
50.
Levenberg, A., Osborne, M.: Stream-based randomised language models for SMT. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 2. Association for Computational Linguistics (2008)
51.
52.
Li, R., Lei, K.H., Khadiwala, R., Chang, K.C.C.: TEDAS: a twitter-based event detection and analysis system. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 1273–1276, April 2012
53.
Luo, G., Tang, C., Yu, P.S.: Resource-adaptive real-time new event detection. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD 2007, p. 497 (2007)
54.
55.
56.
McCreadie, R., Macdonald, C.: Scalable distributed event detection for Twitter. In: 2013 IEEE International Conference on Big Data, 6–9 January 2013. IEEE (2013)
57.
58.
59.
Osborne, M., Petrovic, S.: Bieber no more: first story detection using Twitter and Wikipedia. In: Proceedings of the Workshop on Time-Aware Information Access, TAIA (2012)
60.
61.
62.
Packer, H.S., Samangooei, S., Hare, J.S., Gibbins, N., Lewis, P.H.: Event detection using Twitter and structured semantic query expansion. In: Proceedings of the 1st International Workshop on Multimodal Crowd Sensing, CrowdSens 2012, p. 7 (2012)
63.
Papadopoulos, S., Schinas, E., Mezaris, V., Troncy, R., Kompatsiaris, I.: The 2012 social event detection dataset. In: Proceedings of the 4th ACM Multimedia Systems Conference, pp. 102–107. ACM (2013)
64.
Papadopoulos, S., Troncy, R., Mezaris, V., Huet, B., Kompatsiaris, I.: Social event detection at MediaEval 2011: challenges, dataset and evaluation. In: MediaEval (2011)
65.
Petrovic, S., Osborne, M.: Can twitter replace newswire for breaking news. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media 2011 (2013)
66.
Petrovic, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: Proceedings of the NAACL (2010)
67.
Petrović, 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 (2012)
68.
69.
70.
71.
Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704. ACM (2011)
72.
73.
74.
Schnitzler, F., Liebig, T., Mannor, S., Morik, K.: Combining a Gauss-Markov model and Gaussian process for traffic prediction in Dublin city center. In: Proceedings of the Workshop on Mining Urban Data at the International Conference on Extending Database Technology (2014, to appear)
75.
76.
Signorini, A., Segre, A.M., Polgreen, P.M.: The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. IEEE Trans. Comput. 6(5), e19467 (2011)
77.
Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors [lecture notes]. IEEE Trans. Comput. 25(2), 128–131 (2008)
78.
Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 187–198. VLDB Endowment (2006)
79.
Tang, K., Fei-Fei, L., Koller, D.: Learning latent temporal structure for complex event detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1250–1257. IEEE (2012)
80.
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: ICWSM 2010, 178–185 (2010)
81.
82.
Valkanas, G., Gunopulos, D.: Event detection from social media data. IEEE Transactions on Computers 36(3), 51–58 (2013)
83.
84.
Valkanas, G., Gunopulos, D., Boutsis, I., Kalogeraki, V.: An architecture for detecting events in real-time using massive heterogeneous data sources. In: Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications, BigMine 2013, pp. 103–109 (2013). http://​dl.​acm.​org/​citation.​cfm?​d=​2501221.​2501235
85.
Vavliakis, K.N., Tzima, F.A., Mitkas, P.A.: Event detection via LDA for the MediaEval2012 SED task. In: MediaEval, pp. 5–6 (2012)
86.
Walther, M., Kaisser, M.: Geo-spatial event detection in the twitter stream. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 356–367. Springer, Heidelberg (2013)CrossRef
87.
88.
Watanabe, K., Ochi, M., Okabe, M., Onai, R.: Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2541–2544 (2011). http://​dl.​acm.​org/​citation.​cfm?​id=​2064014
89.
Weng, J., Lee, B.: Event detection in twitter. In: ICWSM (2011)
90.
91.
Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in twitter. In: ICWSM 2010, pp. 355–358 (2010)
92.
Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 28–36. ACM (1998)
93.
94.
95.
Zhong, H., Shi, J., Visontai, M.: Detecting unusual activity in video. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II–819. IEEE (2004)
Metadaten
Titel
Detecting Events in Online Social Networks: Definitions, Trends and Challenges
verfasst von
Nikolaos Panagiotou
Ioannis Katakis
Dimitrios Gunopulos
Copyright-Jahr
2016
Buch
Solving Large Scale Learning Tasks. Challenges and Algorithms

Print ISBN: 978-3-319-41705-9

Electronic ISBN: 978-3-319-41706-6

Copyright-Jahr: 2016

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

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