Detecting Popular Social Events through Limited Observation with Deep Survival Analysis
Paper links
Detecting Popular Social Events through Limited Observation with Deep Survival Analysis — arXiv abstract Detecting Popular Social Events through Limited Observation with Deep Survival Analysis — HTML version Detecting Popular Social Events through Limited Observation with Deep Survival Analysis — PDF Detecting Popular Social Events through Limited Observation with Deep Survival Analysis — DOI
Overview
Project Overview
This research focuses on the fundamental mathematical and statistical challenges of modeling information cascades in network environments. Specifically, we investigate how to model the life cycle of digital information propagation using Deep Survival Analysis. The core objective is to understand the statistical thresholds that dictate whether a digital trend will fade quickly or scale rapidly, relying only on minimal, early-stage data points to reduce computational overhead.
By integrating learning with traditional survival analysis, our methodology offers a more robust statistical framework for time-to-event forecasting in complex networks. This approach shifts the focus away from resource-intensive continuous monitoring toward predictive, mathematically grounded cascade modeling.
Key Methodological Contributions
- Advanced Time-to-Event Modeling: Adapting survival analysis equations to handle the non-linear dynamics of information flow in digital networks.
- Algorithmic Efficiency: Proposing a methodology that requires only a limited initial observation window, significantly reducing the computational resources and memory required to predict network behavior.
- Robust Statistical Baselines: Providing a novel framework for evaluating cascade prediction that outperforms traditional baseline models in data-sparse environments.
Intended Applications and Impact
The methodologies developed in this paper are designed for benign, civilian-focused computational applications. By improving how we predict the volume of digital information cascades, this research supports several infrastructural and commercial sectors:
- E-commerce and Digital Marketing: Assisting commercial analysts in understanding the statistical probability of a product campaign gaining broad organic traction based on early engagement metrics.
- Public Health Communications: Aiding health organizations in forecasting how rapidly public health advisories (e.g., hygiene campaigns) might propagate through a digital ecosystem.
Citation
@misc{ramezani2024detecting,
title={Detecting Popular Social Events through Limited Observation with Deep Survival Analysis},
author={Ramezani, Maryam and Goli, Hossein and Izadi, AmirMohammad and Rabiee, Hamid R.},
year={2024},
eprint={2410.01320},
archivePrefix={arXiv},
primaryClass={cs.SI},
doi={10.48550/arXiv.2410.01320}
}