A Framework for Social Semantic Journalism Final

In the event of breaking news, a wealth of crowd-sourced data, in the form of text, video and image, becomes available on the Social Web. In order to incorporate this data into a news story, the journalist must process, compile and verify content within a very short timespan. Currently this is done manually and is a time-consuming and labour-intensive process for media organisations. This paper proposes Social Semantic Journalism as a solution to the above problem. Semantic metadata, natural language processing (NLP) and other technologies will provide the framework for Social Semantic Journalism to help journalists navigate the overwhelming amount of UGC for detecting known and unknown news events, verifying information and its sources, identify eyewitnesses and contextualising the event and news coverage journalists will be able to bring their professional expertise to this increasingly overwhelming information environment. This paper describes a framework of technologies that can be employed to realise Social Semantic Journalism.