Nic Newman

Identifying and Verifying News through Social Media: Developing a user-centred tool for professional journalists

Identifying and verifying new information quickly are key issues for journalists who use social media. This article examines what tools journalists think they need to cope with the growing volume and complexity of news on social media, and what improvements are needed in existing systems. It gives some initial results from a major European Union research project (Social Sensor), involving computer scientists, journalists, and media researchers, that is designing a new tool to search across social media for news stories, to surface trends, and to help with verification. Preliminary results suggest that an effective tool should focus on the role of key influencers, and should be customisable to suit the particular needs of individual journalists and news organisations.

Challenges of computational verification in social multimedia

Fake or misleading multimedia content and its distribution through social networks such as Twitter constitutes an increasingly important and challenging problem, especially in the context of emergencies and critical situations. In this paper, the aim is to explore the challenges involved in applying a computational verification framework to automatically classify tweets with unreliable media content as fake or real. We created a data corpus of tweets around big events focusing on the ones linking to images (fake or real) of which the reliability could be verified by independent online sources. Extracting content and user features for each tweet, we explored the fake prediction accuracy performance using each set of features separately and in combination. We considered three approaches for evaluating the performance of the classifier, ranging from the use of standard cross-validation, to independent groups of tweets and to cross-event training. The obtained results included a 81% for tweet features and 75% for user ones in the case of cross-validation. When using different events for training and testing, the accuracy is much lower (up to %58) demonstrating that the generalization of the predictor is a very challenging issue.

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

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.

Verifying news on the social web: challenges and prospects

The problem of verification is the key issue for journalists who use social media. This paper argues for the importance of a user-centered approach in finding solutions to this problem. Because journalists have different needs for different types of stories, there is no one magic bullet that can verify social media. Any tool will need to have a multi-faceted approach to the problem, and will have to be adjustable to suit the particular needs of individual journalists and news organizations.

Social Media in the Changing Ecology of News Production and Consumption: The Case in Britain

This paper looks at how the production and consumption of news is changing in the UK. It draws from survey research of individuals in Britain from 2003-2011, which includes evidence on patterns of news readership among Internet users and non-users, as well as more qualitative case studies of developments in online news organizations, based on interviews and log files of journalistic sites. Survey evidence has shown a step-jump in the use of online news since 2003, as a complement to print news reading, but a leveling off since 2009. However, this relative stability in news consumption masks a change in the growing role of social networks, both as a substitute for search in many cases, but also in their relationship with online newspapers, as the interaction of mainstream news and networked individuals has begun to reshape the ecology of production and consumption. Institutionally the paper argues that these patterns underscore recent changes in news media, such as their continued reliance on the Internet, but also added competition from social media, which are becoming a major portal to the Internet. Individually we see the empowerment of networked individuals of a Fifth Estate who have achieved a growing independence from the Fourth Estate as more information moves online and individuals become routinely linked to the Internet. However, a growing synergy between the Fourth and Fifth Estate might be one of the more important aspects of the new news ecology.

Making sense of it all: an attempt to aid journalists in analysing and filtering user generated content

This position paper explores how journalists can embrace new ways of content provision and authoring, by aggregating and analyzing content gathered from Social Media. Current challenges in the news media industry are reviewed and a new system for capturing emerging knowledge from Social Media is described. Novel features that assist professional journalists in processing sheer amounts of Social Media information are presented with a reference to the technical requirements of the system. First implementation steps are also discussed, particularly focusing in event detection and user influence identification.