Detecting Priming News Events
Di Wu, Yiping Ke, Jeffrey Xu Yu, Zheng Liu

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTime Series Analysis and Forecasting · Advanced Text Analysis Techniques · Data Visualization and Analytics
