The rise of big data has led to new demands for machine learning (ML) systems to learn
complex models, with millions to billions of parameters, that promise adequate capacity to …

The problem we tackle in this work is, given a present news event, to generate a plausible
future event that can be caused by the given event. We present a new methodology for …

Clusters in document streams, such as online news articles, can be induced by their textual
contents, as well as by the temporal dynamics of their arriving patterns. Can we leverage …

We describe and evaluate methods for learning to forecast forthcoming events of interest
from a corpus containing 22 years of news stories. We consider the examples of identifying …

Accurately representing the quantity and characteristics of users' interest in certain topics is
an important problem facing topic evolution researchers, particularly as it applies to modern …

When information is abundant, it becomes increasingly difficult to fit nuggets of knowledge
into a single coherent picture. Complex stories spaghetti into branches, side stories, and …

With the availability of cheap location sensors, geotagging of messages in online social
networks is proliferating. For instance, Twitter, Facebook, Foursquare, and Google+ provide …

First story detection (FSD) involves identifying first stories about events from a continuous
stream of documents. A major problem in this task is the high degree of lexical variation in …

There is a rising need for automated analysis of news text, and topic models have proven to
be useful tools for this task. However, as the quality of the topics induced by topic models …

Social media data carries abundant hidden occurrences of real-time events. In this paper, a
novel methodology is proposed for detecting and trending events from tweet clusters that are …