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Thurman, Neil; Schifferes, Steve; Fletcher, Richard; Nic, Newman; Hunt, Stephen; Aljosha, Schapals (23. February 2016): Giving computers a nose for news: exploring the limits of story detection and verification. In: Digital Journalism: pp. 1-11




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 are the subject of this article, which examines the SocialSensor application, a tool developed by a multidisciplinary EU 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 fully recognise the changes to journalists’ sourcing practices brought about by social media, particularly Twitter, and provides some countervailing evidence and an explanation for this failure.