Giving Computers a Nose for News

Thurman, Neil; Schifferes, Steve; Fletcher, Richard; Newman, Nic; Hunt, Stephen P.; Schapals, Aljosha Karim

doi:10.1080/21670811.2016.1149436

Cited by 26 publications

(6 citation statements)

References 9 publications

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“…With the introduction of automation, the ideal of objectivity and its reconsiderations meet a larger discourse that "contrasts inherent human subjectivity with the unthinking automated objectivity of computer programs" (Carlson 2017b, 10), although the journalistic search for sources and information through algorithmic systems is filtered in a particular manner (Thurman et al 2016). The "promise of algorithmic objectivity" is a "carefully crafted fiction" (Gillespie 2014, 179) by large corporate players providing search engines, online social networks and other platforms, contributing to the image of algorithms as hands-off, while they are actually constructed by a particular knowledge logic (168).…”

Section: Objectivity: the Promise Of Algorithmsmentioning

confidence: 99%

Human Still in the Loop

Milosavljević

Vobič

2019

Digital Journalism

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The study investigates how automation novelties in the newsroom both challenge and maintain core values of journalistic professional ideology. On the basis of semi-structured interviews with editors of legacy news institutions in the United Kingdom and Germany the study reveals the rationale of the changing journalism-technology relation as well as the dynamics of the re-articulation of journalism's core ideals. Despite increasing relevance of automation for news production human journalists are still regarded as dominant agents in the routinisation of journalism and the re-articulation of its purpose. The interviewed editors' narratives suggest that they observe technological innovations through wider social, institutional and professional perspectives and appear to be aware that either euphoric or dystopic stances might lead to misconceiving the implications of early automation novelties. On one hand, the editors perceive automation as an opportunity to augment journalism through the combined agency of human actors and automated systems as a way to address the crises of social relevance and business viability. On the other, according to interviewees, automation challenges the ideologisation of professional journalism with respect to the retention of its boundaries and the authority of journalists in contemporary social communication.

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Section: Objectivity: the Promise Of Algorithmsmentioning

confidence: 99%

Human Still in the Loop

Milosavljević

Vobič

2019

Digital Journalism

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“…Thus, there is increasing demand for tools that can assist investigators to timely detect fake content. A significant body of research deals with the automation of news story verification using Web and social media content [9]. One approach is to use tampering detection algorithms for images [8,10] and videos [5].…”

Section: Introductionmentioning

confidence: 99%

Web Video Verification using Contextual Cues

Papadopoulou

Zampoglou

Papadopoulos

et al. 2017

Proceedings of the 2nd International Workshop on Multimedia Forensics and Security

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As news agencies and the public increasingly rely on User-Generated Content, content verification is vital for news producers and consumers alike. We present a novel approach for verifying Web videos by analyzing their online context. It is based on supervised learning on contextual features: one feature set is based on an existing approach for tweet verification adapted to video comments. The other is based on video metadata, such as the video description, likes/dislikes, and uploader information. We evaluate both on a dataset of real and fake videos from YouTube, and demonstrate their effectiveness (F-scores: 0.82, 0.79). We then explore their complementarity and show that under an optimal fusion scheme, the classifier would reach an F-score of 0.9. We finally study the performance of the classifier through time, as more comments accumulate, emulating a real-time verification setting.

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“…Much of the literature discusses the replacement of journalists with robots, which is described as 'disruptive' (Carlson, 2015;Wu et al, 2019). Among concerns about such disruption are ethical questions -for example, copyright and transparency (Thurman et al, 2016) as well as the implications for personalisation of news choices (Thurman, 2011;Zamith, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Tell Me More system extracted text content from published online stories that was presented to journalists in different forms (Iacobelli et al, 2010), and the Story Discovery Engine implements algorithms that support journalists during investigative reporting (Broussard, 2015). The SocialSensor news app surfaced fast moving trends from social media content, but revealed biases therein (Thurman et al, 2016). Many data visualisation tools support journalists to make sense of, for example, social media content (Diakopoulos et al, 2010) and large document releases (e.g.…”

Section: Digital Support Tools For Journalistsmentioning

confidence: 99%

Using computational tools to support journalists’ creativity

et al. 2021

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This paper presents work surrounding INJECT, a newsroom innovation offering digital tools to support journalists. Research showing increasing time and resource pressure on journalists has led to concerns about the demise of investigative reporting and the ability of today’s journalists to interrogate information adequately. Some digital innovations (e.g. tools facilitating robot journalism) have been viewed with suspicion by newsrooms. This paper reports on a research project that seeks to create an innovative tool to support the creative capabilities of time and resource poor journalists. The INJECT project used the advanced information discovery capabilities of digitisation to help journalists find new angles on stories and this paper analyses the extent to which such initiatives might harness digital innovation to benefit both the quality and range of reporting and thereby enhance creativity. It examines the potential of an information processing model of creativity derived from the INJECT tool to assist and support journalists, exploring the theoretical impact as well as the practical implications reported from the newsroom.

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Giving Computers a Nose for News

Cited by 26 publications

References 9 publications

Human Still in the Loop

Human Still in the Loop

Web Video Verification using Contextual Cues

Using computational tools to support journalists’ creativity

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