TwitterSports: Real Time Detection of Key Events from Sports Tweets

  • Jeyakumar Kannan PG and Research Department of Computer Science Jamal Mohamed College Autonomous Tiruchirappalli 620020 INDIA
  • AR.Mohamed Shanavas Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India
  • Sridhar Swaminathan Department of Computer Science and Engineering, Bennett University, Greater Noida, India
Keywords: Social media, Microblogs, Twitter, Event detection, Sports events, Adaptive sliding windows,


Twitter users play a role of human sensors and update information about real-life events by posting their tweets about them. Event detection in Twitter is the process of detecting an event which is an occurrence causing change in the volume of tweets that discuss the associated topic at a specific time and a location by Twitter users. Twitter has been extensively used to detect major social and physical events such as earthquakes, celebrity deaths, presidential elections, traffic jam and others. Real time event detection in Twitter is detecting real-life events from live tweets instantly as soon as the event has occurred. Real time event detection from Cricket sports using Twitter media is an interesting, yet a complex problem. Because, event detection algorithm needs live tweets streamed at real-time about the game and should detect events such as boundary and sixer, at near real-time within few seconds from their occurrences. In this paper, a novel real-time event detection approach is proposed for the Cricket sports domain. The proposed approach first computes the post rate of an adaptive window, which is the ratio between the volumes of tweets in the second half window and the volume of tweets in the first half. An event has occurred if the post rate is above the pre-defined threshold, otherwise the algorithm selects the next big window in an adaptive manner. The predefined threshold helps to filter out the small spikes in the streaming tweets volume. Once an event is detected in a time window along the tweet stream, the event represented inside the window is recognized using the event lexicon representing different events of a cricket game. The proposed real-time event detection algorithm is extensively evaluated on 2017 IPL T20 Cricket sports dataset using ROC and AUC evaluation measures. The experimental results on the performance of the proposed approach show that the adaptive sliding window detects sports events with over 80% true positives and around 15% false positive rates.


(1) Boyd, D. M and N. B. Ellison. Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 2007. 13(1): p. 210–230.

(2) Atefeh, F and Khreich, W. A survey of techniques for event detection in twitter. Computational Intelligence, 2015. 31(1): p. 132-164.

(3) Zhao, S., Zhong, L., Wickramasuriya, J., Vasudevan, V., LiKamWa, R and Rahmati, A. Sportsense: Real-time detection of NFL game events from Twitter. ArXiv preprint, 2012. arXiv:1205.3212.

(4) Zhao, D and M. B. Rosson. How and why people Twitter: The role that micro-blogging plays in informal communication at work. In Proc. ACM International Conference on Supporting Group Work, GROUP ’09, ACM, New York, NY, 2009. p. 243–252.

(5) Hurlock, J and M. Wilson. Searching Twitter: separating the tweet from the chaff. In Proc. International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, 2011.

(6) Jiang, L., M. Yu., M. Zhou., X. Liu and T. Zhao. Target-dependent Twitter sentiment classification. In Proc. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies – vol. 1, HLT ’11, ACL, Stroudsburg, PA, 2011. p. 151–160.

(7) Amer-yahia, S., S. Anjum, A. Ghenai, A. Siddique, S. Abbar, S. Madden, A. Marcus and M. El-haddad. MAQSA: A system for social analytics on news. In Proc. ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, ACM, New York, NY, 2012. p. 653–656.

(8) Tumasjan, A., T. O. Sprenger., P. G. Sandner and I. M. Welpe. Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proc. 4th International Conference on Weblogs and Social Media, ICWSM. The AAAI Press: Washington, DC, 2010.

(9) Wang, X., Gerber, M. S and D. E. Brown. Automatic crime prediction using events extracted from Twitter posts. In Proc. 5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP’12. Springer-Verlag: Berlin, Heidelberg, 2012. p. 231–238.

(10) Troncy, R., B. Malocha and A. T. S. Fialho. Linking events with media. In Proc. 6th International Conference on Semantic Systems, I-SEMANTICS ’10, ACM, New York, NY, 2010. 42: p. 1–42:4.

(11) Zhao, S., Zhong, L., Wickramasuriya, J and Vasudevan, V. Human as

real-time sensors of social and physical events: A case study of twitter and sports games. ArXiv preprint, 2011. arXiv:1106.4300.

(12) Hasan, M, Orgun, M. A and Schwitter, R. A survey on real-time event detection from the Twitter data stream. Journal of Information Science, 2017. 0165551517698564.

(13) T. Sakaki, M. Okazaki and Y. Matsuo. Earthquake shakes Twitter users: real-time event detection by social sensors. In Proc. ACM WWW ’10, 2010.

(14) Y. Qu, C. Huang, P. Zhang and J. Zhang. Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In Proc. ACM 2011 conference on Computer supported cooperative work, 2011.

(15) S. Vieweg, A. L. Hughes, K. Starbird and L. Palen. Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proc. ACM CHI ’10, 2010.

(16) J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman and J. Sperling. TwitterStand: news in tweets. In Proc. ACM SIGSPATIAL, 2009.

(17) J. Hannon, K. McCarthy, J. Lynch and B. Smyth. Personalized and

automatic social summarization of events in video. In Proc. ACM IUI, 2011.

(18) D. Chakrabarti and K. Punera. Event Summarization using Tweets. In Proc. AAAI ICWSM, 2011.

(19) Ekin, A. M. Tekalp and R. Mehrotra. Automatic soccer video analysis and summarization, Image Processing, IEEE Transactions on, 2003. 12: p. 796-807.

(20) Y. Rui, A. Gupta and A. Acero. Automatically extracting highlights for TV Baseball programs. In Proc. ACM Multimedia 2000.

(21) D. Zhang and S.-F. Chang. Event detection in baseball video using superimposed caption recognition. In Proc. ACM Multimedia, 2002.

(22) K. Petridis, S. Bloehdorn, C. Saathoff, N. Simou, S. Dasiopoulou, V.

Tzouvaras, S. Handschuh, Y. Avrithis, Y. Kompatsiaris, and S. Staab. Knowledge representation and semantic annotation of multimedia content. Vision, Image and Signal Processing, IEE Proceedings, 2006. 153: p. 255-262.

(23) C. Xu, Y.-F. Zhang, G. Zhu, Y. Rui, H. Lu and Q. Huang. Using Webcast Text for Semantic Event Detection in Broadcast Sports Video, Multimedia, IEEE Transactions on, 2008. 10: p. 1342-1355

(24) Mathioudakis, M and Koudas, N. TwitterMonitor: Trend Detection over the Twitter Stream. In Proc. SIGMOD/ PODS, 2010. p. 1155–1158.

(25) Weng, J and Lee, B.-S. Event Detection in Twitter. In Proc. ICWSM, 2011. p. 401–408.

(26) Shane Fitzpatrick. Improving new event detection in social streams. 2014. Master Thesis.

(27) Petrovi´c, S., Osborne, M and Lavrenko, V. Streaming First Story Detection with Application to Twitter. In Proc. NAACL HLT, 2010. p. 181–189.

(28) Becker, H., Naaman, M and Gravano, L. Beyond Trending Topics:

Real-Wrold Event Identification on Twitter. In Proc. ICWSM, 2011. 11: p. 438–441.

(29) Cataldi, M., Di Caro, L and Schifanella, C. Emerging Topic Detection on Twitter Based on Temporal and Social Terms Evaluation. In Proc. MDM/KDD, 2010. p. 4:1–10.

(30) Marcus, A., Bernstein, M. S., Badar, O., Karger, D. R., Madden, S and Miller, R. C. TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration. In Proc. CHI, 2011. p. 227–236.

(31) Valkanas, G and Gunopulos, D. How the Live Web Feels About Events. In Proc. In Proc. 22nd ACM International Conference on Information and Knowledge Management CIKM, 2013. p. 639–648.

(32) Popescu, A. M and M. Pennacchiotti. Detecting controversial events from Twitter. In Proc. 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, ACM, New York, NY, 2010. p. 1873–1876.

(33) Benson, E., A. Haghighi and R. Barzilay. Event discovery in social media feeds. In Proc. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, HLT ’11, Association for Computational Linguistics, Stroudsburg, PA, 2011. p.


(34) Lee, R and K. Sumiya. Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In Proc. 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, LBSN ’10, ACM, New York, NY, 2010. p. 1–10.

(35) Sakaki, T., M. Okazaki and Y. Matsuo. Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proc. 19th International Conference on World Wide Web, WWW ’10, ACM, New York, NY, 2010. p. 851–860.

(36) Becker, H., F. Chen, D. Iter, M. Naaman and L. Gravano. Automatic identification and presentation of Twitter content for planned events. In Proc. International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, 2011.

(37) Becker, H., M. Naaman and L. Gravano. Selecting quality Twitter content for events. In Proc. International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, 2011b.

(38) Massoudi, K., M. Tsagkias, M. De Rijke and W. Weerkamp. Incorporating query expansion and quality indicators in searching microblog posts. In Proc. 33rd European Conference on Advances in Information Retrieval, ECIR’11. Springer-Verlag: Berlin, Heidelberg, 2011. p. 362–367.

(39) Weerkamp, W and M. De Rijke. Credibility improves topical blog post retrieval. In Proc. ACL, Columbus, OH, 2008. p. 923–931.

Gu, H., X. Xie, Q. Lv, Y. Ruan and L. Shang. ETree: Effective and efficient event modeling for real-time online social media. In Proc. Web Intelligence and Intelligent Agent Technology, WI-IAT 2011, IEEE/WIC/ACM International Conference, 2011. 1: p. 300–307.