One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method able to collect and summarize data. Sampling the content of an Online Social Network (OSN) plays an important role as a knowledge discovery tool. It is becoming increasingly difficult to ignore the fact that current sampling methods must cope with a lack of a full sampling frame i.e., there is an imposed condition determined by a limited data access. In addition,

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... other key aspect to take into account is the huge amount of data generated by users of social networking services. This type of conditions make especially difficult to develop sampling methods to collect truly reliable data. Therefore, we propose a low computational cost method for sampling emerging global trends on social networking services such as Twitter. The main purpose of this study, is to develop a methodology able to carry out an efficient collecting process via three random generators: Brownian, Illusion and Reservoir. These random generators will be combined with a Metropolis-Hastings Random Walk (MHRW) in order to improve the sampling process. We demonstrate the effectiveness of our approach by correctly providing a descriptive statistics of the collected data. In addition, we also sketch the collecting procedure on real-time carried out on Twitter. Finally, we conclude with a trend concentration graphical description and a formal convergence analysis to evaluate whether the sample of draws has attained an equilibrium state to get a rough estimate of the sample quality.