Bursty Event Detection Model for Twitter
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50583-6_23
Reference31 articles.
1. Comito, C., Forestiero, A., Pizzuti, C.: Bursty event detection in Twitter streams. ACM Trans. Knowl. Disc. Data (TKDD) 13(4), 1–28 (2019)
2. Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey. ACM Comput. Surv. (CSUR) 47(4), 1–38 (2015)
3. Fedoryszak, M., Frederick, B., Rajaram, V., Zhong, C.: Real-time event detection on social data streams. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2774––2782 (2019)
4. Lee, P., Lakshmanan, L.V., Milios, E.E.: Incremental cluster evolution tracking from highly dynamic network data. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 3–14. IEEE (2014)
5. Singh, T., Kumari, M.: Burst: real-time events burst detection in social text stream. J. Supercomput. 77, 1–29 (2021)
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