Linking News across Multiple Streams for Timeliness Analysis

Abstract

Linking multiple news streams based on the reported events and analyzing the streams' temporal publishing patterns are two very important tasks for information analysis, discovering newsworthy stories, studying the event evolution, and detecting untrustworthy sources of information. In this paper, we propose techniques for cross-linking news streams based on the reported events with the purpose of analyzing the temporal dependencies among streams. Our research tackles two main issues: (1) how news streams are connected as reporting an event or the evolution of the same event and (2) how timely the newswires report related events using different publishing platforms. Our approach is based on dynamic topic modeling for detecting and tracking events over the timeline and on clustering news according to the events. We leverage the event-based clustering to link news across different streams and present two scoring functions for ranking the streams based on their timeliness in publishing news about a specific event.