Framework for Real-World Event Detection Through Online Social Networking Sites

  • Ritesh SrivastavaEmail author
  • M. P. S. Bhatia
  • Veena Tayal
  • J. K. Verma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 847)


In recent few years, due to the exponential growth of users on online social networking sites (OSNs), mainly over micro-blogging sites like Twitter, the OSNs now resemble the real world very cohesively. The excess of continuously user-generated online textual data by OSNs that encapsulates almost all verticals of the real world has attracted many researchers who are working in the area of text mining, natural language processing (NLP), machine learning, and data mining. This paper discusses the feasibility of OSNs in detecting real-world events from the horizon of the virtual world formed over OSNs. Moreover, this paper also describes the framework for real-world event detection through online social networking sites.


Online social network Event detection Social network analysis Data mining Text mining 


  1. 1.
    Web 2.0: January 2016, cited 2016. Available from:
  2. 2.
    Kaplan, A.M., Haenlein, M.: Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53(1), 59–68 (2010)CrossRefGoogle Scholar
  3. 3.
    Rosa, K.D., Ellen, J.: Text classification methodologies applied to micro-text in military chat. In: ICMLA’09. International Conference on Machine Learning and Applications. IEEE (2009)Google Scholar
  4. 4.
    Jackoway, A., Samet, H., Sankaranarayanan, J.: Identification of live news events using Twitter. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. ACM (2011)Google Scholar
  5. 5.
    Srivastava, R., et al.: Analyzing Delhi assembly election 2015 using textual content of social network. In: Proceedings of the Sixth International Conference on Computer and Communication Technology. ACM (2015)Google Scholar
  6. 6.
    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. ACM (2010)Google Scholar
  7. 7.
    Weimann, G.: New Terrorism and New Media. Wilson Center Common Labs (2014)Google Scholar
  8. 8.
    O’Connor, B., et al.: From tweets to polls: linking text sentiment to public opinion time series. ICWSM 11(122–129), 1–2 (2010)Google Scholar
  9. 9.
    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. ACM (1998)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.
    Allan, J.: Introduction to topic detection and tracking. In: Topic Detection and Tracking, pp. 1–16. Springer, Berlin (2002)CrossRefGoogle Scholar
  12. 12.
    AlSumait, L., Barbará, D., Domeniconi, C.: On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In: Eighth IEEE International Conference on Data Mining. IEEE (2008)Google Scholar
  13. 13.
    Fiscus, J.G., Doddington, G.R.: Topic detection and tracking evaluation overview. In: Topic Detection and Tracking, pp 17–31. Springer, BerlinCrossRefGoogle Scholar
  14. 14.
    Brants, T., Chen, F., Farahat, A.: A system for new event detection. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2003)Google Scholar
  15. 15.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Comput. Intell. 31(1), 132–164 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Cordeiro, M.: Twitter event detection: combining wavelet analysis and topic inference summarization. In: Doctoral Symposium on Informatics Engineering (2012)Google Scholar
  17. 17.
    Bahir, E., Peled, A.: Real-time major events monitoring and alert system through social networks. J. Conting. Crisis Manag. 23(4), 210–220 (2015)CrossRefGoogle Scholar
  18. 18.
    Cui, L., et al.: Topical event detection on Twitter. In: Australasian Database Conference. Springer, Berlin (2016)Google Scholar
  19. 19.
    Srivastava, R., Bhatia, M.: Ensemble methods for sentiment analysis of on-line micro-texts. In: International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE (2016)Google Scholar
  20. 20.
    Srivastava, R., Bhatia, M.: Challenges with sentiment analysis of on-line micro-texts. Int. J. Intell. Syst. Appl. 9(7), 31 (2017)Google Scholar
  21. 21.
    Srivastava, R., et al.: Exploiting grammatical dependencies for fine-grained opinion mining. In International Conference on Computer and Communication Technology (ICCCT). IEEE (2010)Google Scholar
  22. 22.
    Srivastava, R., Bhatia, M.: Offline versus online sentiment analysis: issues with sentiment analysis of online micro-texts. Int. J. Inf. Retr. Res. (IJIRR) 7(4), 1–18 (2017)Google Scholar
  23. 23.
    Srivastava, R., Bhatia, M.: Real-time unspecified major sub-events detection in the twitter data stream that cause the change in the sentiment score of the targeted event. Int. J. Inf. Technol. Web Eng. (IJITWE) 12(4), 1–21 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ritesh Srivastava
    • 1
    Email author
  • M. P. S. Bhatia
    • 2
  • Veena Tayal
    • 1
  • J. K. Verma
    • 3
  1. 1.CSE Department, FETMRIIRSFaridabadIndia
  2. 2.NSIT, University of DelhiNew DelhiIndia
  3. 3.Galgotias UniversityGreater NoidaIndia

Personalised recommendations