Detecting Priming News Events

TL;DR

This paper presents a method for detecting priming news events by analyzing the correlation between keyword trajectories and a time series index, effectively identifying influential topics and organizing bursty events.

Contribution

It introduces a two-step approach to identify influential topics and detect priming events from document streams and time series data, improving event detection accuracy.

Findings

Successfully detects priming events in real-world data

Effectively identifies influential topics correlated with index movements

Outperforms existing methods in event discovery accuracy

Abstract

We study a problem of detecting priming events based on a time series index and an evolving document stream. We define a priming event as an event which triggers abnormal movements of the time series index, i.e., the Iraq war with respect to the president approval index of President Bush. Existing solutions either focus on organizing coherent keywords from a document stream into events or identifying correlated movements between keyword frequency trajectories and the time series index. In this paper, we tackle the problem in two major steps. (1) We identify the elements that form a priming event. The element identified is called influential topic which consists of a set of coherent keywords. And we extract them by looking at the correlation between keyword trajectories and the interested time series index at a global level. (2) We extract priming events by detecting and organizing the bursty influential topics at a micro level. We evaluate our algorithms on a real-world dataset and the result confirms that our method is able to discover the priming events effectively.