Digital journalism has faced a dramatic change and media companies are challenged to use
data science algorithms to be more competitive in a Big Data era. While this is a relatively …

Large language models have shown astonishing performance on a wide range of reasoning
tasks. In this paper, we investigate whether they could reason about real-world events and …

Many events occur in the world. Some event types are stochastically excited or inhibited—in
the sense of having their probabilities elevated or decreased—by patterns in the sequence …

Large volumes of event data are becoming increasingly available in a wide variety of
applications, such as healthcare analytics, smart cities and social network analysis. The …

Capturing the occurrence dynamics is crucial to predicting which type of events will happen
next and when. A common method to do this is through Hawkes processes. To enhance …

Information diffusion in online social networks is affected by the underlying network
topology, but it also has the power to change it. Online users are constantly creating new …

Hawkes processes are used in statistical modeling for event clustering and causal inference,
while they also can be viewed as stochastic versions of popular compartmental models used …

Twitter has become one of the largest microblogging platforms for users around the world to
share anything happening around them with friends and beyond. A bursty topic in Twitter is …

The ability to predict future user activity is invaluable when it comes to content
recommendation and personalization. For instance, knowing when users will return to an …

The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced
sequences of discrete events. To handle complex domains with many event types, Mei et …