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The behaviour of information cascades (such as retweets) has been modelledextensively. While point process-based generative models have long been in usefor estimating cascade growths, deep learning has greatly enhanced diversefeature integration. We observe two significant temporal signals in cascadedata that have not been emphasized or reported to our knowledge. First, thepopularity of the cascade root is known to influence cascade size strongly; butthe effect falls off rapidly with time. Second, there is a measurable positivecorrelation between the novelty of the root content (with respect to astreaming external corpus) and the relative size of the resulting cascade.Responding to these observations, we propose GammaCas, a new cascade growthmodel as a parametric function of time, which combines deep influence signalsfrom content (e.g., tweet text), network features (e.g., followers of the rootuser), and exogenous event sources (e.g., online news). Specifically, our modelprocesses these signals through a customized recurrent network, whose statesthen provide the parameters of the cascade rate function, which is integratedover time to predict the cascade size. The network parameters are trainedend-to-end using observed cascades. GammaCas outperforms seven recent anddiverse baselines significantly on a large-scale dataset of retweet cascadescoupled with time-aligned online news -- it beats the best baseline with an18.98% increase in terms of Kendall's $\tau$ correlation and $35.63$ reductionin Mean Absolute Percentage Error. Extensive ablation and case studies unearthinteresting insights regarding retweet cascade dynamics.

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