We propose an efficient method for event detection by leveraging a fast feature selection algorithm called CWC. While we begin with word count vectors of authors and words for each time slot (in our case, every hour), we extract discriminative words from each slot using CWC, which vastly reduces the number of features to track. We then convert these word vectors into a time series of vector distances from the initial point. The distance between each time slot and the initial point remains high while an event is happening, yet declines sharply when the event ends, offering an accurate portrait of the span of an event. This method makes it possible to detect events from vast datasets. To demonstrate our method’s effectiveness, we extract events from a dataset of over two hundred million tweets sent in the 21 days following the Great East Japan Earthquake. With CWC, we can identify events from this dataset with great speed and accuracy.
