Computational journalism


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. The field draws on technical aspects of computer science including artificial intelligence, content analysis, visualization, personalization and recommender systems as well as aspects of social computing and information science.

History of the Field

The field emerged at Georgia Institute of Technology in 2006 where a course in the subject was taught by professor Irfan Essa. In February 2008 Georgia Tech hosted a which convened several hundred computing researchers and journalists in Atlanta, GA. In July 2009, The Center for Advanced Study in the Behavioral Sciences at Stanford University hosted a to push the field forward.
Since 2012, Columbia Journalism School has offered a course called for the students enrolled in their The course covers many computer science topics from the perspective of journalism, including document vector space representation, algorithmic and social story selection, language topic models, information visualization, knowledge representation and reasoning, social network analysis, quantitative and qualitative inference, and information security. The to continue its computational journalism program.
Syracuse University launched a , with a mission of preparing students
Stanford University launched a , as well as a course titled, .
In 2017, the Associated Press published a for newsrooms to deploy artificial intelligence and computational methods, a report developed by media strategist

Computational Journalism conferences

In February 2013, the Georgia Institute of Technology held the once again in Atlanta, GA.
In 2014 and 2015, .
In 2016, .
The Google News Lab has sponsored "Computational Journalism Research Awards" within the and in .

Related fields