Sina Weibo, one of the most popular online social networks in China, has recently become a critical medium for Internet users to disseminate and discuss breaking news, social events and other information. Although online social networks and social media have received significant attention from the research community, few studies have focused on Sina Weibo due to the lack of data collection. Given the sheer size of Sina Weibo online social network and vast amount of tweets, retweets and comments, this paper introduces a novel community approach for understanding Sina Weibo online social network. Specifically, we collect all Weibo users registered with Shenzhen as primary geographic location, and build a Shenzhen Weibo community graph based on their following or follower relationships. Our experimental results describe interesting graphical characteristics such as clustering coefficients of this community graph, and reveal the impact of user popularity on tweet influence. Through modeling interactions of Shenzhen Weibo users and their tweeted messages with bipartite graphs and one-mode projections, we analyze the similarity of retweeting and commenting activities among these users, and discuss the implications of the findings on understanding different types of user accounts and the motivations of their following and retweeting behaviors. To the best of our knowledge, this study is the first effort to introduce a community approach for understanding the community characteristics of Sina Weibo and characterizing the similarity of retweeting behaviors and following relationships.