  With the ever-growing volume of online news feeds, event-based organization
of news articles has many practical applications including better information
navigation and the ability to view and analyze events as they develop.
Automatically tracking the evolution of events in large news corpora still
remains a challenging task, and the existing techniques for Event Detection and
Tracking do not place a particular focus on tracking events in very large and
constantly updating news feeds. Here, we propose a new method for robust and
efficient event detection and tracking, which we call RevDet algorithm. RevDet
adopts an iterative clustering approach for tracking events. Even though many
events continue to develop for many days or even months, RevDet is able to
detect and track those events while utilizing only a constant amount of space
on main memory. We also devise a redundancy removal strategy which effectively
eliminates duplicate news articles and substantially reduces the size of data.
We construct a large, comprehensive new ground truth dataset specifically for
event detection and tracking approaches by augmenting two existing datasets:
w2e and GDELT. We implement RevDet algorithm and evaluate its performance on
the ground truth event chains. We discover that our algorithm is able to
accurately recover event chains in the ground-truth dataset. We also compare
the memory efficiency of our algorithm with the standard single pass clustering
approach, and demonstrate the appropriateness of our algorithm for event
detection and tracking task in large news feeds.
