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 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.
Cite
Text
Aravamudan et al. "Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I4.25627Markdown
[Aravamudan et al. "Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/aravamudan2023aaai-anytime/) doi:10.1609/AAAI.V37I4.25627BibTeX
@inproceedings{aravamudan2023aaai-anytime,
title = {{Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods}},
author = {Aravamudan, Akshay and Zhang, Xi and Anagnostopoulos, Georgios C.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {4999-5009},
doi = {10.1609/AAAI.V37I4.25627},
url = {https://mlanthology.org/aaai/2023/aravamudan2023aaai-anytime/}
}