Encyclopedia of Database Systems

Living Edition
| Editors: Ling Liu, M. Tamer Özsu

Temporal Analytics in Social Media

  • Sihem Amer-Yahia
  • Themis Palpanas
  • Mikalai Tsytsarau
  • Sofia Kleisarchaki
  • Ahlame Douzal
  • Vassilis Christophides
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_80708-1

Synonyms

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...

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

Recommended Reading

  1. 1.
    Aggarwal C. Mining text and social streams: a review. SIGKDD Explor Newsl. 2014;15(2):9–19. Chicago, IL.Google Scholar
  2. 2.
    Aggarwal CC, Han J, Wang J, Yu PS. A framework for clustering evolving data streams. In: Proceedings of the 29th I Conference on Very Large Data Bases – Vol. 29, VLDB ‘03. VLDB Endowment; 2003. p. 81–92.Google Scholar
  3. 3.
    Cao F, Ester M, Qian W, Zhou A. Density-based clustering over an evolving data stream with noise. SDM;2006:328–39.Google Scholar
  4. 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.Google Scholar
  5. 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. 6.
    Hulten G, Spencer L, Domingos P. Mining time-changing data streams. In: KDD 01; San Francisco. New York: ACM; 2001.Google Scholar
  7. 7.
    Sadik S, Gruenwald L. Research issues in outlier detection for data streams. SIGKDD Explor Newsl. 2014;15(1):33–40.CrossRefGoogle Scholar
  8. 8.
    Yang D, Rundensteiner E, Ward M. Neighbor-based pattern detection for windows over streaming data. In: EDBT. Saint-Petersburg: ACM; 2009. p. 529–40.Google Scholar
  9. 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.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kontaki M, Gounaris A, Papadopoulos AN, Tsichlas K, Manolopoulos Y. Continuous monitoring of distance-based outliers over data streams. In: ICDE. Hannover: IEEE Computer Society; 2011. p. 135–46.Google Scholar
  11. 11.
    Brzeziński D. Mining data streams with concept drift. PhD thesis, Poznan University of Technology. 2010.Google Scholar
  12. 12.
    Ada I, Berthold MR. EVE: a framework for event detection. Evol Syst. 2013;4(1): 61–70.Google Scholar
  13. 13.
    Benhardus J, Kalita J. Streaming trend detection in twitter. IJWBC. 2013; 9(1):122–39.Google Scholar
  14. 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 International Conference on Web Search and Data Mining (WSDM); Seattle, WA; 2012.Google Scholar
  15. 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; San Diego, CA. ACM; 2010. p. 57–64.Google Scholar
  16. 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.CrossRefGoogle Scholar
  17. 17.
    Mathioudakis M, Koudas N. TwitterMonitor: trend detection over the twitter stream. SIGMOD ’10. p. 1155–8. http://www.l2f.inesc-id.pt/~fmmb/wiki/uploads/Work/misnis.ref11.pdf
  18. 18.
    Alvanaki F, Sebastian M, Ramamritham K, Weikum G. EnBlogue: emergent topic detection in web 2.0 streams. SIGMOD ’11; Athens, Greece.Google Scholar
  19. 19.
    Varlamis I, Vassalos V, Palaios A. Monitoring the evolution of interests in the blogosphere. In: ICDE Workshops; Cancun, Mexico. IEEE Computer Society; 2008. p. 513–518.Google Scholar
  20. 20.
    Thelwall M, Buckley K, Paltoglou G. Sentiment in twitter events. JASIST. 2011;62(2):406–18.CrossRefGoogle Scholar
  21. 21.
    Tsytsarau, et al. DiversiWeb11. In: Mikalai Tsytsarau, Themis Palpanas, Kerstin Denecke. Scalable detection of sentiment-based contradictions. In: International Workshop on Knowledge Diversity on the Web (DiversiWeb), in conjunction with the World Wide Web Conference (WWW), Hyberabad, Mar 2011.Google Scholar
  22. 22.
    Tsytsarau, et al. Sigmod14. In: Mikalai Tsytsarau, Sihem Amer-Yahia, Themis Palpanas. Efficient sentiment correlation for large-scale demographics. ACM SIG International conference on Management of Data/Principles of Database Systems (SIGMOD/PODS); New York; 2013.Google Scholar
  23. 23.
    Tsytsarau, et al. KDD14. In: Mikalai Tsytsarau, Themis Palpanas, Malu Castellanos. Dynamics of news events and social media reaction. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), New York, Aug 2014.Google Scholar
  24. 24.
    Zhu Y Shasha D. Statstream: statistical monitoring of thousands of data streams in real time. In: VLDB; Hong Kong, China; 2002. p. 358–69.Google Scholar
  25. 25.
    Zliobaite I. Learning under concept drift: an overview. 2010. CoRR abs/1010.4784Google Scholar
  26. 26.
    Lakshmanan LVS, Pei J, Zhao Y. QC-trees: an efficient summary structure for semantic OLAP. SIGMOD Conference; 2003. p. 64–75.Google Scholar
  27. 27.
    Hawwash B, Nasraoui O. Stream-dashboard: a framework for mining, tracking and validating clusters in a data stream. BigMine. 2012. p. 109–17Google Scholar
  28. 28.
    Kifer D, Ben-David S, Gehrke J. Detecting change in data streams. VLDB. 2004, p. 180–191.Google Scholar
  29. 29.
    Mustafa A, Haque A, Khan L, Baron M. Evolving stream classification using change detection. CollaborateCom. 2014. p. 154–62.Google Scholar
  30. 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. 31.
    Choudhury MD, Sundaram H, John A, Seligmann DD. Multi-scale characterization of social network dynamics in the blogosphere, in CIKM. 2008. p. 1515–6.Google Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Sihem Amer-Yahia
    • 1
  • Themis Palpanas
    • 2
  • Mikalai Tsytsarau
    • 3
  • Sofia Kleisarchaki
    • 1
  • Ahlame Douzal
    • 1
  • Vassilis Christophides
    • 4
  1. 1.CNRS, Univ. Grenoble AlpsGrenobleFrance
  2. 2.Université Paris DescartesParisFrance
  3. 3.University of TrentoPovoItaly
  4. 4.INRIA ParisParisFrance