Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods

Abstract

Predicting user engagement – whether a user will engage in a given information cascade – is an important problem in the context of social media, as it is useful to online marketing and misinformation mitigation just to name a few major applications. Based on split population multi-variate survival processes, we develop a discriminative approach that, unlike prior works, leads to a single model for predicting whether individual users of an information network will engage a given cascade for arbitrary forecast horizons and observation periods. Being probabilistic in nature, this model retains the interpretability of its generative counterpart and renders count prediction intervals in a disciplined manner. Our results indicate that our model is highly competitive, if not superior, to current approaches, when compared over varying observed cascade histories and forecast horizons.

Akshay Aravamudan

Doctor of Philosophy in Computer Engineering

My interests include machine learning, stochastic point processes, social-media diffusion and influence, hydrology applications, and edge AI.

Xi Zhang

Doctor of Philosophy in Electrical Engineering

My research interests include point process analysis, modeling and optimization.

Georgios C. Anagnostopoulos

Associate Professor of Electrical Engineering & Computer Science

I lead the Machine Learning Research Group at FIT.