# Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals

arXiv: https://arxiv.org/abs/2106.07012 · HTML: https://mlsanity.com/paper/2106.07012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 59 references that are in the map.
- First posted on arXiv 13 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Jump Stochastic Differential Equations](https://mlsanity.com/paper/1905.10403/) (2019)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [Unconstrained Monotonic Neural Networks](https://mlsanity.com/paper/1908.05164/) (2019)
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity](https://mlsanity.com/paper/1602.06033/) (2016)
- [Popularity Prediction on Social Platforms with Coupled Graph Neural Networks](https://mlsanity.com/paper/1906.09032/) (2019)
- [Topological Recurrent Neural Network for Diffusion Prediction](https://mlsanity.com/paper/1711.10162/) (2017)
- [Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes](https://mlsanity.com/paper/1401.0778/) (2014)
- [Feature Driven and Point Process Approaches for Popularity Prediction](https://mlsanity.com/paper/1608.04862/) (2016)
- [SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations](https://mlsanity.com/paper/1711.01679/) (2017)
- [TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics](https://mlsanity.com/paper/1603.09449/) (2016)
- [A Bayesian approach for predicting the popularity of tweets](https://mlsanity.com/paper/1304.6777/) (2013)
- [Predicting Successful Memes using Network and Community Structure](https://mlsanity.com/paper/1403.6199/) (2014)
- [Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models](https://mlsanity.com/paper/1910.05451/) (2019)
- [Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes](https://mlsanity.com/paper/1503.02754/) (2015)
- [Neural Diffusion Model for Microscopic Cascade Prediction](https://mlsanity.com/paper/1812.08933/) (2018)
- [Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction](https://mlsanity.com/paper/2006.07812/) (2020)
- [The statistical physics of discovering exogenous and endogenous factors in a chain of events](https://mlsanity.com/paper/2003.00659/) (2020)

## Abstract (arXiv)

The behaviour of information cascades (such as retweets) has been modelled extensively. While point process-based generative models have long been in use for estimating cascade growths, deep learning has greatly enhanced diverse feature integration. We observe two significant temporal signals in cascade data that have not been emphasized or reported to our knowledge. First, the popularity of the cascade root is known to influence cascade size strongly; but the effect falls off rapidly with time. Second, there is a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade. Responding to these observations, we propose GammaCas, a new cascade growth model as a parametric function of time, which combines deep influence signals from content (e.g., tweet text), network features (e.g., followers of the root user), and exogenous event sources (e.g., online news). Specifically, our model processes these signals through a customized recurrent network, whose states then provide the parameters of the cascade rate function, which is integrated over time to predict the cascade size. The network parameters are trained end-to-end using observed cascades. GammaCas outperforms seven recent and diverse baselines significantly on a large-scale dataset of retweet cascades coupled with time-aligned online news -- it beats the best baseline with an 18.98% increase in terms of Kendall's τ correlation and 35.63 reduction in Mean Absolute Percentage Error. Extensive ablation and case studies unearth interesting insights regarding retweet cascade dynamics.