MGP: Extracting Multi-Granular Phases for Evolutional Events on Social Network Platforms

Abstract

In this paper, we proposes a system for extracting multi-granular phases for event evolutions on social network platforms like Sina Weibo and Twitter. Existing studies on event extraction usually use a set of tweets to describe an event, which is not able to present the evolutional knowledge about the event. In many decision-making scenarios, it is much helpful to detect the evolutional stage of an event, as this can help people make counter-measures according to the current developing trend of the event. In this paper, we present a multi-granular approach for extracting the phases of evolutional events. We implement a web-based prototype called MGP (Multi-Granular Phase) which can extract and show the stages of events from a fine granularity such as hour to a coarse granularity like month. After a brief introduction on the architecture of MGP, we present the implemental details of MGP. Then, we present a case study to demonstrate the usability and effectiveness of MGP.