On Informative Tweet Identification For Tracking Mass Events
This work addresses the challenge of filtering uninformative tweets for users tracking real-time mass events, representing an incremental improvement over existing methods.
The paper tackled the problem of identifying informative tweets for tracking mass events by comparing traditional handcrafted features with automatically learned features and proposing a hybrid model. The experiments showed that the latter approaches significantly outperformed the former, with the hybrid model performing best, suggesting effective mechanisms for event tracking.
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.