How it Happened: Discovering and Archiving the Evolution of a Story Using Social Signals.

Social networks like Twitter and Facebook are the largest sources of public opinion and real-time information on the Internet. If an event is of general interest, news articles follow and eventually a Wikipedia page. We propose the problem of automatic event story generation and archiving by combining social and news data to construct a new type of document in the form of a Wiki-like page structure. We introduce a technique that shows the evolution of a story as perceived by the crowd in social media, along with editorially authored articles annotated with examples of social media as supporting evidence. At the core of our research, is the temporally sensitive extraction of data that serve as context for retrieval purposes. Our approach includes a fine-grained vote counting strategy that is used for weighting purposes, pseudo-relevance feedback and query expansion with social data and web query logs along with a timeline algorithm as the base for a story. We demonstrate the effectiveness of our approach by processing a dataset comprising millions of English language tweets generated over a one year period and present a full implementation of our system.