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Encyclopedia of Database Systems pp 3909–3912Cite as

Temporal Analytics in Social Media

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Synonyms

Change detection; Concept evolution; Trend detection; Novelty detection

Definition

Social media represent a valuable source of subjective user-generated content since they reflect opinions, beliefs, findings, or experiences of a large number of users on a wide range of topics. Temporal analytics of social media content aims to provide insights regarding the dynamics of user conversations in different mining tasks over the vocabulary of words employed in the corresponding posts. For example, a time-aware analysis of social media posts will enable to recognize popular conversation trends over a period of time; to alert about emerging topics that are fast gathering momentum; to monitor how topics of particular interest evolve; to trace changes in key aspects of conversation summaries, such as user opinions and sentiments; or to identify relationships among these summaries (e.g., correlations).

Historical Background

Temporal analytics of social media can been seen as a branch of...

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Recommended Reading

  1. Aggarwal C. Mining text and social streams: a review. SIGKDD Explor Newsl. 2014;15(2):9–19. Chicago, IL.

    Google Scholar 

  2. Aggarwal CC, Han J, Wang J, Yu PS. A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases; 2003. p. 81–92.

    Chapter  Google Scholar 

  3. Cao F, Ester M, Qian W, Zhou A. Density-based clustering over an evolving data stream with noise. In: Proceedings of the 6th SIAM International Conference on Data Mining; 2006. p. 328–39.

    Google Scholar 

  4. Forestiero A, Pizzuti C, Spezzano G. A single pass algorithm for clustering evolving data streams based on swarm intelligence. Data Min Knowl Discov. 2013;26(1):1–26.

    Article  MathSciNet  Google Scholar 

  5. Kleisarchaki S, Kotzinos D, Tsamardinos I, Christophides V. A methodological framework for statistical analysis of social text streams. In: Information search, integration and personalization, LNCS. Heidelberg: Springer Berlin; 2013. p. 101–10.

    Google Scholar 

  6. Hulten G, Spencer L, Domingos P. Mining time-changing data streams. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001.

    Google Scholar 

  7. Sadik S, Gruenwald L. Research issues in outlier detection for data streams. SIGKDD Explor Newsl. 2014;15(1):33–40.

    Article  Google Scholar 

  8. Yang D, Rundensteiner E, Ward M. Neighbor-based pattern detection for windows over streaming data. In: Advances in Database Technology, Proceedings of the 12th International Conference on Extending Database Technology; 2009. p. 529–40.

    Google Scholar 

  9. Angiulli F, Fassetti F. Distance-based outlier queries in data streams: the novel task and algorithms. Data Min Knowl Disc. 2010;20(2):290–324.

    Article  MathSciNet  Google Scholar 

  10. Kontaki M, Gounaris A, Papadopoulos AN, Tsichlas K, Manolopoulos Y. Continuous monitoring of distance-based outliers over data streams. In: Proceedings of the 27th International Conference on Data Engineering; 2011. p. 135–46.

    Google Scholar 

  11. Brzeziński D. Mining data streams with concept drift. PhD thesis, Poznan University of Technology. 2010.

    Google Scholar 

  12. Ada I, Berthold MR. EVE: a framework for event detection. Evol Syst. 2013;4(1): 61–70.

    Article  Google Scholar 

  13. Benhardus J, Kalita J. Streaming trend detection in twitter. IJWBC. 2013; 9(1):122–39.

    Article  Google Scholar 

  14. Saha A, Sindhwani V. Learning evolving and emerging topics in social media: a dynamic NMF approach with temporal regularization. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining; 2012.

    Google Scholar 

  15. Goorha S, Ungar L. Discovery of significant emerging trends. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2010. p. 57–64.

    Google Scholar 

  16. Naaman M, Becker H, Gravano L. Hip and trendy: characterizing emerging trends on twitter. J Am Soc Inf Sci Technol. 2011;62(5):902–18.

    Article  Google Scholar 

  17. Mathioudakis M, Koudas N. TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2010. p. 1155–8. http://www.l2f.inesc-id.pt/~fmmb/wiki/uploads/Work/misnis.ref11.pdf

  18. Alvanaki F, Sebastian M, Ramamritham K, Weikum G. EnBlogue: emergent topic detection in web 2.0 streams. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2011.

    Google Scholar 

  19. Varlamis I, Vassalos V, Palaios A. Monitoring the evolution of interests in the blogosphere. In: Proceedings of the Workshops of 24th International Conference on Data Engineering; 2008. p. 513–518.

    Google Scholar 

  20. Thelwall M, Buckley K, Paltoglou G. Sentiment in twitter events. JASIST. 2011;62(2):406–18.

    Article  Google Scholar 

  21. Tsytsarau, et al. DiversiWeb11. In: Mikalai Tsytsarau, Themis Palpanas, Kerstin Denecke. Scalable detection of sentiment-based contradictions. In: Proceedings of the International Workshop on Knowledge Diversity on the Web (DiversiWeb), in conjunction with the World Wide Web Conference (WWW); 2011.

    Google Scholar 

  22. Tsytsarau, et al. In: Mikalai Tsytsarau, Sihem Amer-Yahia, Themis Palpanas. Efficient sentiment correlation for large-scale demographics. Proceedings of the ACM SIGMOD International Conference on Management of Data/Proceedings of the 32nd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2013.

    Google Scholar 

  23. Tsytsarau, et al. In: Mikalai Tsytsarau, Themis Palpanas, Malu Castellanos. Dynamics of news events and social media reaction. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2014.

    Google Scholar 

  24. Zhu Y Shasha D. Statstream: statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 358–69.

    Chapter  Google Scholar 

  25. Zliobaite I. Learning under concept drift: an overview. 2010. CoRR abs/1010.4784

    Google Scholar 

  26. Lakshmanan LVS, Pei J, Zhao Y. QC-trees: an efficient summary structure for semantic OLAP. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2003. p. 64–75.

    Google Scholar 

  27. Hawwash B, Nasraoui O. Stream-dashboard: a framework for mining, tracking and validating clusters in a data stream. In: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications; 2012. p. 109–17

    Google Scholar 

  28. Kifer D, Ben-David S, Gehrke J. Detecting change in data streams. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004. p. 180–191.

    Chapter  Google Scholar 

  29. Mustafa A, Haque A, Khan L, Baron M. Evolving stream classification using change detection. In: Proceedings of the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing; 2014. p. 154–62.

    Google Scholar 

  30. Choudhury, et al. Examine sentiment biases in blogosphere’s communities, relying on the entropy measure as an indicator of the diversity in opinions.

    Google Scholar 

  31. Choudhury MD, Sundaram H, John A, Seligmann DD. Multi-scale characterization of social network dynamics in the blogosphere. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management; 2008. p. 1515–6.

    Google Scholar 

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Author information

Authors and Affiliations

  1. CNRS, Univ. Grenoble Alps, Grenoble, France

    Sihem Amer-Yahia, Sofia Kleisarchaki & Ahlame Douzal

  2. Laboratoire d’Informatique de Grenoble, CNRS-LIG, Saint Martin-d’Hères, Grenoble, France

    Sihem Amer-Yahia

  3. Paris Descartes University, Paris, France

    Themis Palpanas

  4. University of Trento, Povo, Italy

    Mikalai Tsytsarau

  5. INRIA Paris-Roquencourt, Paris, France

    Vassilis Christophides

Authors
  1. Sihem Amer-Yahia
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  2. Themis Palpanas
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  3. Mikalai Tsytsarau
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  4. Sofia Kleisarchaki
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  5. Ahlame Douzal
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  6. Vassilis Christophides
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Corresponding author

Correspondence to Sihem Amer-Yahia .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. IBM Almaden Research Center, San Jose, CA, USA

    Fatma Özcan

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Cite this entry

Amer-Yahia, S., Palpanas, T., Tsytsarau, M., Kleisarchaki, S., Douzal, A., Christophides, V. (2018). Temporal Analytics in Social Media. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80708

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