Mining Newsworthy Topics From Social Media

Newsworthy stories are increasingly being shared through social networking platforms such as Twitter and Reddit, and journalists now use them to rapidly discover stories and eye-witness accounts. We present a technique that detects "bursts" of phrases on Twitter that is designed for a real-time topic-detection system. We describe a time-dependent variant of the classic tf-idf approach and group together bursty phrases that often appear in the same messages in order to identify emerging topics. We demonstrate our methods by analysing tweets corresponding to events drawn from the worlds of politics and sport, as well as more general mainstream news. We created a user-centred "ground truth" to evaluate our methods, based on mainstream media accounts of the events. This helps ensure our methods remain practical. We compare several clustering and topic ranking methods to discover the characteristics of news-related collections, and show that different strategies are needed to detect emerging topics within them. We show that our methods successfully detect a range of different topics for each event and can retrieve messages (for example, tweets) that represent each topic for the user.