Abstract This paper presents an unsupervised architecture for retrieving and ranking conceptually related tweets which can be used in real time. We present a model for ranking tweets with respect to topic relevance in order to improve the accuracy of information extraction. The proposed architecture uses concept enrichment from a knowledge source in order to expand the concept beyond the search keywords. The enriched concept is used to determine similarity levels between tweets and the given concept followed by a ranking of those tweets based on different similarity values. Tweets above a certain similarity threshold are considered as useful for providing relevant information (this is not part of this paper). We obtained precision values up to 0.81 and F values up to 0.61 for a tweet corpus of 2400 Tweets on the topic related to 2014 NZ general elections.