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Event Detection in Twitter: Methodological Evaluation and Structural Analysis of the Bibliometric Data

  • Musa Ibarhim M. Ishag
  • Kwang Sun Ryu
  • Jong Yun Lee
  • Keun Ho RyuEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 769)

Abstract

Twitter—a social networking service is increasingly becoming an important source of news and information for various aspects of our life. However, harnessing reliable sources is both tedious and challenging. Algorithms for mining and detecting events from Twitter have been developed. In this paper, event detection techniques are investigated. In essence, a theoretical comparison of the state-of-the-art event detection algorithms is performed along with highlights to the current issues and proper suggestions to mitigate them. In addition, a knowledge domain map analysis using CiteSpace is applied to the bibliometric data in the field in order to explore the structural dynamics of the research in this domain.

Keywords

Twitter Event detection Bibliometric analysis CiteSpace 

Notes

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826), and MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2013-0-00881) supervised by the IITP (Institute for Information & Communications Technology Promotion).

References

  1. 1.
    Thackeray, R., et al.: Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month. BMC Cancer 13(1), 508 (2013).  https://doi.org/10.1186/1471-2407-13-508CrossRefGoogle Scholar
  2. 2.
    Loff, J., Reis, M., Martins, B.: Predicting well-being with geo-referenced data collected from social media platforms. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1167–1173. ACM, Salamanca, Spain (2015).  https://doi.org/10.1145/2695664.2695939
  3. 3.
    Earle, P.S., Bowden, D.C., Guy, M.: Twitter earthquake detection: earthquake monitoring in a social world. Ann. Geophys. 54(6), 708–715 (2012).  https://doi.org/10.4401/ag-5364Google Scholar
  4. 4.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011).  https://doi.org/10.1016/j.jocs.2010.12.007
  5. 5.
    Aiello, L.M., et al.: Sensing trending topics in Twitter. IEEE Trans. Multimedia 15(6), 1268–1282 (2013).  https://doi.org/10.1109/TMM.2013.2265080CrossRefGoogle Scholar
  6. 6.
    Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection, vol. 589. Wiley (2005)Google Scholar
  7. 7.
    McMinn, A.J., Moshfeghi, Y., Jose, J.M.: Building a large-scale corpus for evaluating event detection on twitter. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pp. 409–418 ACM, San Francisco, California, USA (2013).  https://doi.org/10.1145/2505515.2505695
  8. 8.
    Rei, L., Grobelnik, M., Mladenić, D.: Event Detection in Twitter With an Event Knowledge BaseGoogle Scholar
  9. 9.
    Wong, W.-K., Neill, D.B.: Tutorial on Event Detection KDD 2009. Age 9 (2009)Google Scholar
  10. 10.
    Allan, J.: Topic Detection and Tracking: Event-Based Information Organization, vol. 12. Springer Science & Business Media (2012)Google Scholar
  11. 11.
    Hogenboom, F., et al.: A survey of event extraction methods from text for decision support systems. Decis. Support Syst. 85, 12–22 (2016).  https://doi.org/10.1016/j.dss.2016.02.006CrossRefGoogle Scholar
  12. 12.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Comput. Intell. 31(1), 132–164 (2015).  https://doi.org/10.1111/coin.12017MathSciNetCrossRefGoogle Scholar
  13. 13.
    Imran, M., et al.: Processing social media messages in mass emergency: a survey. ACM Comput. Surv. (CSUR) 47(4), 67 (2015).  https://doi.org/10.1145/2771588MathSciNetCrossRefGoogle Scholar
  14. 14.
    Cordeiro, M., Gama, J.: Online social networks event detection: a survey. In: Solving Large Scale Learning Tasks. Challenges and Algorithms, pp. 1–41. Springer International Publishing (2016).  https://doi.org/10.1007/978-3-319-41706-6_1
  15. 15.
    Chen, C.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57(3), 359–377 (2006).  https://doi.org/10.1002/asi.20317
  16. 16.
    Balaid, A., et al.: Knowledge maps: a systematic literature review and directions for future research. Int. J. Inf. Manage. 36(3), 451–475 (2016).  https://doi.org/10.1016/j.ijinfomgt.2016.02.005CrossRefGoogle Scholar
  17. 17.
    Chen, H., et al.: Terrorism Informatics: Knowledge Management and Data Mining for Homeland Security, vol. 18. Springer Science & Business Media (2008)Google Scholar
  18. 18.
    Liu, W., et al.: Collaboration pattern and topic analysis on intelligence and security informatics research. IEEE Intell. Syst. 29(3), 39–46 (2014).  https://doi.org/10.1109/MIS.2012.106CrossRefGoogle Scholar
  19. 19.
    Qian, D., et al.: Mapping knowledge domain analysis of medical informatics education. In: Frontier and Future Development of Information Technology in Medicine and Education, pp. 2209–2213. Springer Netherlands (2014).  https://doi.org/10.1007/978-94-007-7618-0_269
  20. 20.
    Lee, Y.-C., Chen, C., Tsai, X.-T.: Visualizing the knowledge domain of nanoparticle drug delivery technologies: a scientometric review. Appl. Sci. 6(1), 11 (2016).  https://doi.org/10.3390/app6010011
  21. 21.
    Singh, V.K., et al.: Scientometric mapping of research on ‘Big Data’. Scientometrics 105(2), 727–741 (2015).  https://doi.org/10.1007/s11192-015-1729-9CrossRefGoogle Scholar
  22. 22.
    Silva, T.H.P., Moro, M.M., Silva, A.P.C.: Authorship contribution dynamics on publication venues in computer science: an aggregated quality analysis. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1142–1147. ACM, Salamanca, Spain (2015).  https://doi.org/10.1145/2695664.2695781
  23. 23.
    Federico, P., et al.: A survey on visual approaches for analyzing scientific literature and patents. IEEE Trans. Visual Comput. Graph. 23(9), 2179–2198 (2016).  https://doi.org/10.1109/TVCG.2016.2610422CrossRefGoogle Scholar
  24. 24.
    Chen, C., Leydesdorff, L.: Patterns of connections and movements in dual-map overlays: a new method of publication portfolio analysis. J. Assoc. Inf. Sci. Technol. 65(2), 334–351 (2014).  https://doi.org/10.1002/asi.22968CrossRefGoogle Scholar
  25. 25.
    Chen, C.: Predictive effects of structural variation on citation counts. J. Am. Soc. Inf. Sci. Technol. 63(3), 431–449 (2012).  https://doi.org/10.1002/asi.21694
  26. 26.
    Chen, C., Ibekwe-SanJuan, F., Hou, J.: The structure and dynamics of cocitation clusters: a multiple-perspective cocitation analysis. J. Am. Soc. Inform. Sci. Technol. 61(7), 1386–1409 (2010).  https://doi.org/10.1002/asi.21309CrossRefGoogle Scholar
  27. 27.
    Chen, C.: Searching for intellectual turning points: progressive knowledge domain visualization. Proc. Natl. Acad. Sci. 101(suppl 1), 5303–5310 (2004)Google Scholar
  28. 28.
    Chen, C., Dubin, R., Kim, M.C.: Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin. Biol. Ther. 14(9), 1295–1317 (2014).  https://doi.org/10.1517/14712598.2014.920813CrossRefGoogle Scholar
  29. 29.
    Reuters, T.: Web of Science (2012)Google Scholar
  30. 30.
    Chen, H., et al.: Terrorism Informatics: Knowledge Management and Data Mining for Homeland Security. Springer Science & Business Media (2008)Google Scholar
  31. 31.
    Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)Google Scholar
  32. 32.
    Campello, R.J.G.B., Hruschka, E.R.: A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets Syst. 157(21), 2858–2875 (2006).  https://doi.org/10.1016/j.fss.2006.07.006
  33. 33.
    Aizawa, A.: An information-theoretic perspective of tf–idf measures. Inf. Process. Manag. 39(1), 45–65 (2003),.  https://doi.org/10.1016/s0306-4573(02)00021-3
  34. 34.
    Quackenbush, S.R., Barnwell, T.P., Clements, M.A.: Objective Measures of Speech Quality. Prentice Hall (1988)Google Scholar
  35. 35.
    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, Raleigh, North Carolina, USA (2010).  https://doi.org/10.1145/1772690.1772777
  36. 36.
    Sankaranarayanan, J., et al.: Twitterstand: news in tweets. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 42–51. ACM, Seattle, Washington, USA (2009).  https://doi.org/10.1145/1653771.1653781
  37. 37.
    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), vol. 3, pp. 120–123. IEEE, Toronto, ON, Canada (2010).  https://doi.org/10.1109/wi-iat.2010.205
  38. 38.
    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. ACM, San Jose, California, USA (2010).  https://doi.org/10.1145/1867699.1867701
  39. 39.
    Li, R., et al.: Tedas: a twitter-based event detection and analysis system. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 1273–1276. IEEE, Washington, DC, USA (2012).  https://doi.org/10.1109/icde.2012.125
  40. 40.
    Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 1155–1158. ACM, Indianapolis, Indiana, USA (2010).  https://doi.org/10.1145/1807167.1807306
  41. 41.
    Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 181–189. Association for Computational Linguistics, Los Angeles, California, USA (2010)Google Scholar
  42. 42.
    Marcus, A., et al.: Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 227–236. ACM, Vancouver, BC, Canada (2011).  https://doi.org/10.1145/1978942.1978975
  43. 43.
    Watanabe, K., et al.: 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. ACM, Glasgow, Scotland, UK (2011).  https://doi.org/10.1145/2063576.2064014
  44. 44.
    Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on twitter based on temporal and social terms evaluation. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining. ACM, Washington, DC, USA (2010).  https://doi.org/10.1145/1814245.1814249
  45. 45.
    Gaglio, S., Lo Re, G., Morana, M.: Real-time detection of twitter social events from the user’s perspective. In: 2015 IEEE International Conference on Communications (ICC), pp. 1207–1212. IEEE, London, UK (2015).  https://doi.org/10.1109/icc.2015.7248487
  46. 46.
    Lee, Y., Nam, K.W., Ryu, K.H.: Fast mining of spatial frequent wordset from social database. Spat. Inf. Res. 25(2), 271–280 (2017).  https://doi.org/10.1007/s41324-017-0094-6CrossRefGoogle Scholar
  47. 47.
    Petkos, G., et al.: A soft frequent pattern mining approach for textual topic detection. In: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14), p. 25. ACM, Thessaloniki, Greece (2014).  https://doi.org/10.1145/2611040.2611068
  48. 48.
    Huang, J., Peng, M., Wang, H.: Topic detection from large scale of microblog stream with high utility pattern clustering. In: Proceedings of the 8th Workshop on Ph.D. Workshop in Information and Knowledge Management, pp. 3–10. ACM, Melbourne, Australia (2015).  https://doi.org/10.1145/2809890.2809894
  49. 49.
    Yun, U., Ryang, H., Ho Ryu, K.: High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates. Expert Syst. Appl. 41(8), 3861–3878 (2014).  https://doi.org/10.1016/j.eswa.2013.11.038CrossRefGoogle Scholar
  50. 50.
    Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 43–52. ACM, San Diego, California, USA (1999).  https://doi.org/10.1145/312129.312191
  51. 51.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans. Knowl. Data Eng. 25(4), 919–931 (2013).  https://doi.org/10.1109/TKDE.2012.29CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Musa Ibarhim M. Ishag
    • 1
  • Kwang Sun Ryu
    • 1
  • Jong Yun Lee
    • 2
  • Keun Ho Ryu
    • 1
    Email author
  1. 1.College of Electrical and Computer EngineeringChungbuk National UniversityCheongjuSouth Korea
  2. 2.Department of Software EngineeringChungbuk National UniversityCheongjuSouth Korea

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