Discovering influential users in micro-blog marketing with influence maximization mechanism

Micro-blog marketing has become a main business model for social networks nowadays. On social networking sites (e.g., Twitter), micro-blog marketing enables the advertisers to put ads to attract customers to buy their products. During this process, a rather key step for the success of advertisers is to conduct marketing researches to discover which micro-blog users are their potential customers who can greatly promote their products to other customers so that the advertising investment can be greatly reduced. This problem is considered as “influence maximization” issue. In this paper and in attempt to discover the influential users in micro-blog marketing, we try to analyze the influences of nodes in a micro-blog network and propose a Community Scale-Sensitive Maxdegree (CSSM) algorithm for maximizing the influences when placing ads. Experimental results on the very hot micro-blog service (i.e., Twitter dataset) demonstrate that our proposed CSSM algorithm significantly outperforms other related node selection strategies, in terms of the influence spread and time complexity.