- Accepted Version
Restricted to Repository staff only until 23 August 2018.
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The use of social media as a source of news is entering a new phase as computer algorithms are developed and deployed to detect, rank, and verify news. The efficacy and ethics of such technology is the subject of this article, which examines the SocialSensor application, a tool developed by a multidisciplinary European Union research project. The results suggest that computer software can be used successfully to identify trending news stories, allow journalists to search within a social media corpus, and help verify social media contributors and content. However, such software also raises questions about accountability as social media is algorithmically filtered for use by journalists and others. Our analysis of the inputs SocialSensor relies on shows biases towards those who are vocal and have an audience, many of whom are men in the media. We also reveal some of the technology’s temporal and topic preferences. The conclusion discusses whether such biases are necessary for systems like SocialSensor to be effective. The article also suggests that academic research has failed to recognise fully the changes to journalists’ sourcing practices brought about by social media, particularly Twitter, and provides some countervailing evidence and an explanation for this failure.
|Additional Information:||This is an Accepted Manuscript of an article published by Taylor & Francis in Digital Journalism, first published online on 23/02/2016, available online: http://www.tandfonline.com/10.1080/21670811.2016.1149436.|
|Uncontrolled Keywords:||algorithmic news, automation, computerisation, employment, journalism, social media, topic detection, verification|
|Subjects:||P Language and Literature
Z Bibliography. Library Science. Information Resources > ZA Information resources
|Divisions:||School of Arts > Department of Journalism|
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