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Online discussions are valuable resources to study user behaviour on adiverse set of topics. Unlike previous studies which model a discussion in astatic manner, in the present study, we model it as a time-varying process andsolve two inter-related problems -- predict which user groups will get engagedwith an ongoing discussion, and forecast the growth rate of a discussion interms of the number of comments. We propose RGNet (Relativistic GravitationalNerwork), a novel algorithm that uses Einstein Field Equations of gravity tomodel online discussions as `cloud of dust' hovering over a user spacetimemanifold, attracting users of different groups at different rates over time. Wealso propose GUVec, a global user embedding method for an online discussion,which is used by RGNet to predict temporal user engagement. RGNet leveragesdifferent textual and network-based features to learn the dust distribution fordiscussions.  We employ four baselines -- first two using LSTM architecture, third oneusing Newtonian model of gravity, and fourth one using a logistic regressionadopted from a previous work on engagement prediction. Experiments on Redditdataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average errorfor temporal engagement prediction of user groups and growth rate forecasting,respectively, outperforming all the baselines significantly. We further employRGNet to predict non-temporal engagement -- whether users will comment to agiven post or not. RGNet achieves 0.62 AUC for this task, outperformingexisting baseline by 8.77% AUC.

Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory