Using algorithms for story discovery heralds a new phase for social media says Neil Thurman and his co-authors. They look at the implications when algorithms assess social posts and take a detailed look at one in particular, SocialSensor.
Their study examines accountability and bias. They identify a gap in academic research regarding the influences social media has had on journalistic sourcing. They make recommendations for remediation.
- Neil Thurman, Department of Communication Studies and Media Research, LMU, Munich
- Steve Schifferes, Department of Journalism, City University, London
- Richard Fletcher, Reuters Institute for the Study of Journalism, Oxford
- Nic Newman, Reuters Institute for the Study of Journalism, Oxford
- Stephen Hunt, Institute of Education, University College, London
- Aljosha Karim Schapals,Department of Journalism, City University, London
‘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.’