The literature considers multi-type Markov branching processes (BPs), where the offspring distribution depends only on the living (current) population. We analyse the total-current population-dependent BPs where the offspring distribution can also depend on the total (dead and living) population. Such a generalization is inspired by the need to accurately model content propagation over online social networks (OSNs). The key question investigated is the time-asymptotic proportion of the populations, which translates to the proportional visibility of the posts on the OSN. We provide the answer using a stochastic approximation (SA) technique, which has not been used in the existing BP literature. The analysis is derived using a non-trivial autonomous measurable ODE. Interestingly, we prove the possibility of a new limiting behaviour for the stochastic trajectory, named as hovering around. Such a result is not just new to the theory of BPs but also to the SA based literature. Later, we explore three new variants of BPs: (i) any living individual of a population can attack and acquire the living individuals of the other population, in addition to producing its offspring; (ii) the individuals can die due to abnormal circumstances, and not just at the completion of their lifetimes; (iii) the expected number of offspring decreases as the total-population increases, leading to the saturation of the total-population. Such variants aid in analysing unexplored aspects of content propagation over OSNs: (i) competition in advertisement posts for similar products; (ii) controlling fake-post propagation, while not affecting the sharing of real-post; (iii) impact of re-forwarding the posts. We also designed and analysed a participation (mean-field) game where the OSN lures the users with a reward-based scheme to provide their opinion about the actuality of the post (fake or real).