Steve Schifferes

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.

The Future of Personalization at News Websites: Lessons from a Longitudinal Study

This paper tracks the recent history of personalization at national news websites in the United Kingdom and United States, allowing an analysis to be made of the reasons for and implications of the adoption of this form of adaptive interactivity. Using three content surveys conducted over three and a half years, the study records—at an unprecedented level of detail—the range of personalization features offered by contemporary news websites, and demonstrates how news organizations increasingly rely on software algorithms to predict readers’ content preferences. The results also detail how news organizations’ deployment of personalization on mobile devices, and in conjunction with social networking platforms, is still at an early stage. In addressing the under-researched but important—and increasingly prevalent—phenomenon of personalization, this paper contributes to debates on journalisms future funding, transparency, and societal benefits.

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.

Downloading disaster: BBC news online coverage of the global financial crisis

The financial crisis that began in autumn 2008 has attracted considerable attention in regard to the role of the media. This article examines both the audience and the content of the coverage of the crisis on the BBC News website, the largest online news provider in the UK. It demonstrates that online news was a significant part of the overall media coverage of the crisis. Online consumption patterns are very different from those of other media, but the claim that online audiences are ‘dumbed down’ or that they were not provided with a sophisticated range of information and analysis is critically examined. The study also questions whether the content of news coverage was as negative as has been suggested. The research is based on unique access to the BBC News web server-logs, which allow researchers to track audiences not only for the online site as a whole, but also for individual stories, and to match that to content analysis. It makes an important contribution to providing evidence-based research to examine the competing claims that have been made about the role of the business media in the financial crisis.

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.

Building the ‘Truthmeter’: Training algorithms to help journalists assess the credibility of social media sources

Social media is now used as an information source in many different contexts. For professional journalists, the use of social media for news production creates new challenges for the verification process. This article describes the development and evaluation of the ‘Truthmeter’ – a tool that automatically scores the journalistic credibility of social media contributors in order to inform overall credibility assessments. The Truthmeter was evaluated using a three-stage process that used both qualitative and quantitative methods, consisting of (1) obtaining a ground truth, (2) building a description of existing practices and (3) calibration, modification and testing. As a result of the evaluation process, which could be generalized and applied in other contexts, the Truthmeter produced credibility scores that were closely aligned with those of trainee journalists. Substantively, the evaluation also highlighted the importance of ‘relational’ credibility assessments, where credibility may be attributed based on networked connections to other credible contributors.

Social media, journalism and the public

This paper draws on the parallels between the current period and other periods of historic change in journalism to examine what is new in todays world of social media and what continuities there are with the past. It examines the changing relationship between the public and the press and how it is being continuously reinterpreted. It addresses the questions of whether we are the beginning or end of a process of revolutionary media change.