Giving computers a nose for news: Exploring the limits of story detection and verification

Thurman, Neil, Schifferes, Steve, Fletcher, Richard, Newman, Nic, Hunt, Stephen, & Schapals, Aljosha Karim (2016) Giving computers a nose for news: Exploring the limits of story detection and verification. Digital Journalism, 4(7), pp. 838-848.

View at publisher


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.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 98707
Item Type: Journal Article
Refereed: Yes
DOI: 10.1080/21670811.2016.1149436
ISSN: 2167-082X
Divisions: Current > Research Centres > Digital Media Research Centre
Current > QUT Faculties and Divisions > Creative Industries Faculty
Current > Schools > Journalism, Media & Communication
Deposited On: 07 Sep 2016 23:07
Last Modified: 08 Sep 2016 21:27

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page