Tracking information flow in financial text

Information is fundamental to Finance, and understanding how it flows from official sources to news agencies is a central problem. Readers need to digest information rapidly from high volume news feeds, which often contain duplicate and irrelevant stories, to gain a competitive advantage. We propose a text categorisation task over pairs of official announcements and news stories to identify whether the story repeats announcement information and/or adds value. Using features based on the intersection of the texts and relative timing, our system identifies information flow at 89.5 % F-score and three types of journalistic contribution at 73.4 % to 85.7 % F-score. Evaluation against majority annotator decision performs 13 % better than a bag-of-words baseline.