A major event often has repercussions on both news media and microblogging sites such as Twitter. Reports from mainstream news agencies and discussions from Twitter complement each other to form a complete picture. An event can have multiple aspects (sub-events) describing it from multiple angles, each of which attracts opinions/comments posted on Twitter. Mining such reflections is interesting to both policy makers and ordinary people seeking information. In this paper, we propose a unified framework to mine multi-aspect reflections of news events in Twitter. We propose a novel and efficient dynamic hierarchical entity-aware event discovery model to learn news events and their multiple aspects. The aspects of an event are linked to their reflections in Twitter by a bootstrapped dataless classification scheme, which elegantly handles the challenges of selecting informative tweets under overwhelming noise and bridging the vocabularies of news and tweets. In addition, we demonstrate that our framework naturally generates an informative presentation of each event with entity graphs, time spans, news summaries and tweet highlights to facilitate user digestion.