Core Data Science


Popularity Prediction for Social Media over Arbitrary Time Horizons

We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content’s popularity at one or more reference horizons in tandem with a point predictor of an effective growth parameter that reflects the timescale of popularity growth.


Post Approvals in Online Communities

Through a longitudinal analysis of 233,402 Facebook Groups, we examined 1) the factors that led to a community adopting post approvals and 2) how the setting shaped subsequent...


Equilibria in Auctions with Ad Types

This paper studies equilibrium quality of semi-separable position auctions (known as the Ad Types setting [9]) with greedy or optimal allocation combined with generalized...