Electrical Engineering and Computer Science Faculty Publications

Document Type

Conference Proceeding

Publication Title

Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023

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. Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

First Page

4999

DOI

10.1609/aaai.v37i4.25627

Publication Date

2023

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