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Event summarization for sports games using twitter streams

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Abstract

Given a textual data stream related to an event, social event summarization aims to generate an informative textual description that can capture all the important moments, and it plays a critical role in mining and analyzing social media streams. In this paper, we present a general social event summarization framework using Twitter streams. The proposed framework consists of three key components: participant detection, sub-event detection, and summary tweet extraction. To make the system applicable in real data, an online clustering approach is developed for participant detection and an online temporal-content mixture model is proposed to conduct sub-event detection. Experiments show that the proposed framework can achieve similar performance with its batch counterpart.

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Notes

  1. http://projects.ldc.upenn.edu/TDT/

  2. We use “participant sub-events” and “global sub-events” respectively to represent the important moments happened on the participant-level and on the entire event-level. A “global sub-event” may consist of one or more “participant sub-events”. For example., the “steal” action in the basketball game typically involves both the defensive and offensive players, and can be generated by merging the two participant-level sub-events.

  3. We use the algorithm described in [27] as a baseline and ad hoc spike detection algorithm.

  4. β was set to 5 minutes in our experiments.

  5. β was set to 5 minutes in our experiments.

  6. https://dev.twitter.com/docs/streaming-apis

  7. http://espn.go.com/nba/scoreboard

  8. http://espn.go.com/nba/scoreboard

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Acknowledgements

The work was supported in part by the National Science Foundation under Grant Nos. IIS-1213026, CNS-1126619, and CNS-1461926, Chinese National Natural Science Foundation under grant 91646116, Ministry of Education/China Mobile joint research grant under Project No.5-10, and Scientific and Technological Support Project (Society) of Jiangsu Province (No. BE2016776).

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Authors and Affiliations

  1. Jiangsu BDSIP Key Lab, School of Computer Science, Nanjing University of Posts and Telecommunications, No.9 Wenyuan Road, Nanjing, Jiangsu, 210023, People’s Republic of China

    Yue Huang & Tao Li

  2. School of Computing and Information Sciences, Florida International University, 11200 SW 8th Street, Miami, FL, 33199, USA

    Chao Shen & Tao Li

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  1. Yue Huang
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Correspondence to Tao Li.

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Huang, Y., Shen, C. & Li, T. Event summarization for sports games using twitter streams. World Wide Web 21, 609–627 (2018). https://doi.org/10.1007/s11280-017-0477-6

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