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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