Real-time Detection and Sorting of News on Microblogging Platforms

Due to the increasing popularity of microblogging platforms (e.g., Twitter), detecting realtime news from microblogs (e.g., tweets) has recently drawn a lot of attention. Most of the previous work on this subject detect news by analyzing propagation patterns of microblogs. This approach has two limitations: (i) many non-news microblogs (e.g. marketing activities) have propagation patterns similar to news microblogs and therefore they can be falsely reported as news; (ii) using propagation patterns to identify news involves a time delay until the pattern is formed, therefore news are not detected in real time. We propose an alternative approach, which, motivated by the necessity of real-time detection of news, does not rely on propagation of posts. Moreover, we propose a real-time sorting strategy that orders the detected news microblogs using a translational approach. An experimental evaluation on a large-scale microblogging dataset demonstrates the effectiveness of our approach.