Computational journalism

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Computational journalism can be defined as the application of computation to the activities of journalism such as information gathering, organization, sensemaking, communication and dissemination of news information, while upholding values of journalism such as accuracy and verifiability.[1] The field draws on technical aspects of computer science including artificial intelligence, content analysis (NLP, vision, audition), visualization, personalization and recommender systems as well as aspects of social computing and information science.

History of the Field[edit]

The field emerged at Georgia Institute of Technology in 2006 where a course in the subject was taught by professor Irfan Essa.[2] In February 2008 Georgia Tech hosted a Symposium on Computation and Journalism which convened several hundred computing researchers and journalists in Atlanta, GA. In July 2009, The Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford University hosted a workshop to push the field forward.[3]

In the spring of 2012, Columbia Journalism School taught a course called Frontiers of Computational Journalism for the students enrolled in their dual degree in CS and journalism. Topics covered included document vector space representation, algorithmic and social story selection (filtering), language topic models, information visualization, knowledge representation and reasoning, social network analysis, quantitative and qualitative inference, and information security. In February 2013, the Georgia Institute of Technology held the Computational Journalism Symposium once again in Atlanta, GA.

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