# TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics

arXiv: https://arxiv.org/abs/1603.09449 · HTML: https://mlsanity.com/paper/1603.09449/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 15 of 15)

- [Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes](https://mlsanity.com/paper/2105.11152/) (2021)
- [Interval-censored Hawkes processes](https://mlsanity.com/paper/2104.07932/) (2021)
- [Temporal Attention Augmented Transformer Hawkes Process](https://mlsanity.com/paper/2112.14472/) (2021)
- [SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge](https://mlsanity.com/paper/2405.10497/) (2024)
- [Universal Transformer Hawkes Process with Adaptive Recursive Iteration](https://mlsanity.com/paper/2112.14479/) (2021)
- [Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models](https://mlsanity.com/paper/2201.05044/) (2022)
- [Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches](https://mlsanity.com/paper/2501.14291/) (2025)
- [Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data](https://mlsanity.com/paper/2111.02062/) (2021)
- [Will You Dance To The Challenge? Predicting User Participation of TikTok Challenges](https://mlsanity.com/paper/2112.13384/) (2021)
- [Hawkes Process Based on Controlled Differential Equations](https://mlsanity.com/paper/2305.07031/) (2023)
- [Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction](https://mlsanity.com/paper/2308.09976/) (2023)
- [Non-Parametric Estimation of Multi-dimensional Marked Hawkes Processes](https://mlsanity.com/paper/2402.04740/) (2024)
- [Massively Parallel Exact Inference for Hawkes Processes](https://mlsanity.com/paper/2604.01342/) (2026)
- [Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals](https://mlsanity.com/paper/2106.07012/) (2021)
- [How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process](https://mlsanity.com/paper/2112.03008/) (2021)

## Abstract (arXiv)

Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the temporal patterns of retweet activity for an original tweet. We model the system by Time-Dependent Hawkes process (TiDeH), which properly takes into account the circadian nature of the users and the aging of information. The input of the prediction model are observed retweet times and structural information about the underlying social network. We develop a procedure for parameter optimization and for predicting the future profiles of retweet activity at different time resolutions. We validate our methodology on a large corpus of Twitter data and demonstrate its systematic improvement over existing approaches in all the time regimes.