| Summary: | 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 help those journalists and editors. 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, identifying 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 by
journalists and editors to realise Social Semantic Journalism.
|
|---|
