A Method for Building Burst-Annotated Co-Occurrence Networks for Analysing Trends in Textual Data

This paper presents a method for constructing a specific type of language resources that are conveniently applicable to analysis of trending topics in time-annotated textual data. More specifically, the method consists of building a co-occurrence network from the on-line content (such as New York Times articles) that conform to key words selected by users (e.g., {`}Arab Spring{'}). Within the network, burstiness of the particular nodes (key words) and edges (co-occurrence relations) is computed. A service deployed on the network then facilitates exploration of the underlying text in order to identify trending topics. Using the graph structure of the network, one can assess also a broader context of the trending events. To limit the information overload of users, we filter the edges and nodes displayed by their burstiness scores to show only the presumably more important ones. The paper gives details on the proposed method, including a step-by-step walk through with plenty of real data examples. We report on a specific application of our method to the topic of `Arab Spring{'} and make the language resource applied therein publicly available for experimentation. Last but not least, we outline a methodology of an ongoing evaluation of our method.