μTOP: Spatio-Temporal Detection and Summarization of Locally Trending Topics in Microblog Posts

User-generated content in social media can offer valuable insights into local trends, events, and topics of interest. However, navigating through the vast amounts of posts either to retrieve certain pieces of information or to obtain an overview of the existing content, is often a challenging and overwhelming task. In this work, we present μTOP, a system for detecting and summarizing locally trending topics in microblog posts based on spatial, temporal and textual criteria. Using a sliding window model over an incoming stream of posts, μTOP detects locally trending topics, and associates each one with a spatio-temporal footprint. Then, for each spatial region and time period in which a certain topic is trending, the system generates a summary of the relevant posts, by selecting top-k posts based on the criteria of coverage and diversity. μTOP includes a Web-based user interface, providing a comprehensive way to visualize and explore the detected topics and their spatio-temporal summaries via a map and a timeline. The functionality of the system will be demonstrated using a continuously updated dataset containing more than 30 million geotagged tweets.