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Detecting Popular Social Events through Limited Observation with Deep Survival Analysis

Maryam Ramezani, Hossein Goli, AmirMohammad Izadi, Hamid R. Rabiee

arXivOctober 2, 2024Preprint

Abstract

Identifying and analysing popular trends in social networks gives valuable insight into the dynamics of information dissemination. More importantly, by observing the dissemination pattern of a piece of information in the early stages of its expansion, it becomes possible to determine whether a cascade will become highly popular in the future. This research predicts and detects popular trends in social networks by observing limited early-stage data with a deep survival analysis based method. The proposed method is evaluated on real-world anonymised datasets from Twitter, Weibo and Digg, and is applicable to recommendation systems, reach prediction for digital content, and decision-making in digital marketing.

Predicting which cascade becomes popular is usually framed as classification over a fixed observation window. Survival analysis is a better fit: the quantity of interest is when — and whether — a cascade crosses a popularity threshold, and most observations are censored because the cascade is still running.

The model is trained on early-stage observations only, and evaluated on Twitter, Weibo and Digg.

Cite this paper

@misc{ramezani2024detecting,
  title = {{Detecting Popular Social Events through Limited Observation with Deep Survival Analysis}},
  author = {Maryam Ramezani and Hossein Goli and AmirMohammad Izadi and Hamid R. Rabiee},
  eprint = {2410.01320},
  archivePrefix = {arXiv},
  year = {2024},
  url = {https://arxiv.org/abs/2410.01320}
}

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