  Twitter has been heavily used as an important channel for communicating and
discussing about events in real-time. In such major events, many uninformative
tweets are also published rapidly by many users, making it hard to follow the
events. In this paper, we address this problem by investigating machine
learning methods for automatically identifying informative tweets among those
that are relevant to a target event. We examine both traditional approaches
with a rich set of handcrafted features and state of the art approaches with
automatically learned features. We further propose a hybrid model that
leverages both the handcrafted features and the automatically learned ones. Our
experiments on several large datasets of real-world events show that the latter
approaches significantly outperform the former and our proposed model performs
the best, suggesting highly effective mechanisms for tracking mass events.
