Capturing News Stories Once, Retelling a Thousand Ways

Abstract

We live in a constantly evolving world where news stories and relevant facts are happening every moment. For each of those stories, numerous news articles, posts, and social media reactions are created, offering a multitude of viewpoints about what is happening around us. Many applications have tried to deal with this complexity from very different angles, targeting particular needs, reconstructing certain parts of the story, and exploiting certain visualization paradigms. In this paper, we identify those challenges and study how an adequate news story representation can effectively support the different phases of the news consumption process. We propose an innovative model called News Semantic Snapshot (NSS) that is designed to capture the entire context of a news item. This model can feed very different applications assisting the users before, during, and after the news story consumption. It formalizes a duality in the news annotations that distinguishes between representative entities and relevant entities, and considers different relevancy dimensions that are incorporated into the model in the form of concentric layers. Finally, we analyze the impact of this NSS on existing prototypes and how it can support future ones.