On Hierarchical Visualization of Event Detection in Twitter

The data generated from social networking services like Twitter, contains rich information of all kinds of events. Studies have been made for event detection events in Twitter, the focus being primarily to detect events and visually align them along a timeline. Since the events can be relatively large in number carrying unequal importance, it might be overwhelming for the user to go through all the events along the timeline. A better approach could be, if the user can get an overview of the timeline at different levels of detail and traverse to those segments in which he is more interested. In this paper, we propose a novel unified workflow in which events are detected and a hierarchy of the detected events is generated through recursive hierarchical clustering. The levels of hierarchy represent the timeline at different granularities of time. Comprehensive experiment on Twitter dataset demonstrates the effectiveness of our framework.

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