Mobile microblog browsing is inconvenient due to the limitations of mobile devices. Therefore, it is important to effectively retrieve relevant and timely microblog content that caters to the information need of mobile users. In this paper, we first present the findings from a large-scale study on the relationship between microblog content and user context. Then, we show a system that detects local microblog topics, estimates user interests and selects user-preferred topics. The system employs user context to detect microblog topics and post-processes the topics for finding user-preferred content. We exploit time, location, browsing history, social relationship and activity as user context. The effectiveness of our approach is evaluated against several baseline algorithms for investigating the impact of user context on the relevance of retrieved topics. According to our experimental results, the approach enhances the relevance of topics by 24%, compared to the baseline approaches. Thus, we expect that the proposed approach is helpful in advancing mobile information retrieval.