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Influence maximization in online social networks is a trending research area due to its use in many real-world domains. Influence maximization addresses the problem of identifying a k-size subset of nodes in a social network which can trigger a cascade of further adoptions, leading to maximum influence spread across the social network. In this paper, influence maximization has been proposed by combining a node’s connections and its actual past activity pattern. Analyzing node’s activity with respect to interaction frequency and self-similarity trend, provides a more realistic view of the node’s influence potential. Inspired by this concept, HAC-Rank algorithm has been proposed for identification of initial adopters based on both their connections and past behaviour. Furthermore, a Hurst-based Influence Maximization (HBIM) model for diffusion, wherein a node’s activation depends upon its connections and the self-similarity trend exhibited by its past activity, has also been proposed. For assessing the self-similarity trend in a node’s activity pattern, Hurst exponent (H) has been computed. Based on the results achieved, proposed algorithm has been found to perform better than other state-of-art algorithms for initial adopter identification.