PGraph: A Graph-based Structure for Interactive Event Exploration on Social Media

Abstract

Event detection is a common research topic in visualization. Existing methods always follow an exploration mode, where machine learning algorithms identify events and then analyze them via a visualization system. The detection process does not integrate the expert's experience. In this paper, we propose a novel framework that organizes the original dataset as an integrated graph that allows for Interactive Event Detection (IED) on the graph. Specifically, we formulate the problem Interactive Event Detection as subgraph detection on the graph under expert's interactions. Further, we define a flexible structure called PGraph to model the dataset and then propose an efficient algorithm that returns a subgraph as an event. Our proposed method supports performing various IED tasks under the expert's interactions. We evaluate the utility of our approach by applying it in two scenarios. One uses a social media dataset to study hot events; the other urban burglary dataset is used to detect consecutive burglary cases. Case studies show that our algorithm could detect more global events considering the expert's experience. By quantitative performance experiments, our method outperforms traditional machine detection approaches, especially in the social media dataset; our method's accuracy is higher than baselines at least 10%.