62 research outputs found

    Financial capability, the financial crisis, and trust in news media

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    Since the Global Financial Crisis of 2008 the financial media has been analysed from the perspectives of experts and far less from the audiences who consume it. This article fills this gap by exploring public consumption of financial news and their levels of satisfaction. It explores another, less researched, issue; that of financial literacy, which is a major impediment to public understanding and is weaker among women, young people and the less affluent. Consequently, the study makes the following suggestions: financial journalism needs to respond to a wide audience and provide more useful, unbiased and accessible financial news; personal finance news, which is an under-researched genre, could build financial capability levels and might improve trust between media and its audiences; and the financial media should be considered a key player by policy-makers if they want to bolster financial capabilit

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

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    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 threestage 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

    Giving Computers a Nose for News: Exploring the limits of story detection and verification

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    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
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