Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods
Akshay Aravamudan, Xi Zhang, Georgios C. Anagnostopoulos
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 couple 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.
BibTeX
@article{Aravamudan_Zhang_Anagnostopoulos_2023, title={Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/25627}, DOI={10.1609/aaai.v37i4.25627}, abstractNote={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 couple 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.}, number={4}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Aravamudan, Akshay and Zhang, Xi and Anagnostopoulos, Georgios C.}, year={2023}, month={Jun.}, pages={4999-5009} }