Social media platform are increasingly becoming a rich source of information for capturing the views and opinions of online customers. Major brands listen to the social streams to understand the general pulse of their online community. The foremost task here is to construct a "filter" to fetch the brand-relevant data from the social streams. Due to the nature of social platforms, simple filters/queries for retrieval yield a lot of noise leading to a need for complicated filters. Constructing such complicated filters is a non-trivial task and requires significant time-investment from a social marketer. In this paper, we propose a method to automate this task by expanding a seed set of watch keywords to maximize the number of retrieved relevant social feeds around the brand and combining them appropriately into a social query. We show the strengths and weaknesses of the proposed approach in the light of real-world social feeds for various brands.