Relativistic Gravitation Based on Symmetry We present a Relativistic Newtonian Dynamics (    R N D    ) for motion of objects in a gravitational field generated by a moving source. As in General Relativity (    G R    ), we assume that objects move by a geodesic with respect to some metric, which is defined by the field. This metric is defined on flat lab spacetime and is derived using only symmetry, the fact that the field propagates with the speed of light, and the Newtonian limit. For a field of a single source, the influenced direction of the field at spacetime point x is defined as the direction from x to the to the position of the source at the retarded time. The metric depends only on this direction and the strength of the field at x. We show that for a static source, the     R N D     metric is of the same form as the Whitehead metric, and the Schwarzschild metric in Eddingtonxe2x80x93Finkelstein coordinates. Motion predicted under this model passes all classical tests of     G R    . Moreover, in this model, the total time for a round trip of light is as predicted by     G R    , but velocities of light and object and time dilation differ from the     G R     predictions. For example, light rays propagating toward the massive object do not slow down. The new time dilation prediction could be observed by measuring the relativistic redshift for stars near a black hole and for sungrazing comets. Terrestrial experiments to test speed of light predictions and the relativistic redshift are proposed. The     R N D     model is similar to Whiteheadxe2x80x99s gravitation model for a static field, but its proposed extension to the nonstatic case is different. This extension uses a complex fourpotential description of fields propagating with the speed of light.

Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory Online discussions are valuable resources to study user behaviour on a diverse set of topics. Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a timevarying process and solve two interrelated problems xe2x80x93 predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments. We propose RGNet (Relativistic Gravitational Nerwork), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as u0027cloud of dustu0027 hovering over a user spacetime manifold, attracting users of different groups at different rates over time. We also propose GUVec, a global user embedding method for an online discussion, which is used by RGNet to predict temporal user engagement. RGNet leverages different textual and networkbased features to learn the dust distribution for discussions. We employ four baselines xe2x80x93 first two using LSTM architecture, third one using Newtonian model of gravity, and fourth one using a logistic regression adopted from a previous work on engagement prediction. Experiments on Reddit dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error for temporal engagement prediction of user groups and growth rate forecasting, respectively, outperforming all the baselines significantly. We further employ RGNet to predict nontemporal engagement xe2x80x93 whether users will comment to a given post or not. RGNet achieves 0.62 AUC for this task, outperforming existing baseline by 8.77% AUC.