Media REVEALr: A Social Multimedia Monitoring and Intelligence System for Web Multimedia Verification

Andreadou, Katerina; Papadopoulos, Symeon; Apostolidis, Lazaros; Krithara, Anastasia; Kompatsiaris, Ioannis

doi:10.1007/978-3-319-18455-5_1

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Reveal project [47] has been similarly concerned with providing tools to support journalistic work in the context of the propagation of false information and rumors. Media REVEALr [3] does provide certain kinds of verification support. This includes the detection of duplicate images against an indexed collection, grouping of posts by their similarity, and grouping and visualization of specific entities associated with an event.…”

Section: Introductionmentioning

confidence: 99%

Supporting the Use of User Generated Content in Journalistic Practice

Tolmie

Procter

Randall

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Social media and user-generated content (UGC) are increasingly important features of journalistic work in a number of different ways. However, their use presents major challenges, not least because information posted on social media is not always reliable and therefore its veracity needs to be checked before it can be considered as fit for use in the reporting of news. We report on the results of a series of in-depth ethnographic studies of journalist work practices undertaken as part of the requirements gathering for a prototype of a social media verification 'dashboard' and its subsequent evaluation. We conclude with some reflections upon the broader implications of our findings for the design of tools to support journalistic work.

show abstract

Section: Introductionmentioning

confidence: 99%

Supporting the Use of User Generated Content in Journalistic Practice

Tolmie

Procter

Randall

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…It was concerned with verification of social media content from a journalistic and enterprise , especially focusing on image verification. The project produced a number of publications on journalistic verification practices concerning social media(Brandtzaeg et al 2016), social media verification approaches(Andreadou et al 2015), and approaches to track down the location of social media users(Middleton and Krivcovs 2016).-InVID 22 (In Video Veritas) is a Horizon 2020 project, funded by the European Commission (2017-2020), which will build a platform providing services to detect, authenticate, and check the reliability and accuracy of newsworthy video files and video content spread via social media. -CrossCheck 23 is a collaborative verification project led by First Draft and Google News Lab, in collaboration with a number of newsrooms in France, to fight misinformation, with an initial focus on the French presidential election.…”

mentioning

confidence: 99%

Detection and Resolution of Rumours in Social Media

et al. 2018

View full text Add to dashboard Cite

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

show abstract

“…While this review mainly focuses on verification of textual content associated with news, there has been work on multimedia verification, including images and video. For more details on this line of research, the reader is referred to [105,106,107,108] (image) and [109,110] (video).…”

Section: Validation and Verification Of Contentmentioning

confidence: 99%

Mining social media for newsgathering: A review

Zubiaga

2019

Online Social Networks and Media

View full text Add to dashboard Cite

Social media is becoming an increasingly important data source for learning about breaking news and for following the latest developments of ongoing news. This is in part possible thanks to the existence of mobile devices, which allows anyone with access to the Internet to post updates from anywhere, leading in turn to a growing presence of citizen journalism. Consequently, social media has become a go-to resource for journalists during the process of newsgathering. Use of social media for newsgathering is however challenging, and suitable tools are needed in order to facilitate access to useful information for reporting. In this paper, we provide an overview of research in data mining and natural language processing for mining social media for newsgathering. We discuss five different areas that researchers have worked on to mitigate the challenges inherent to social media newsgathering: news discovery, curation of news, validation and verification of content, newsgathering dashboards, and other tasks. We outline the progress made so far in the field, summarise the current challenges as well as discuss future directions in the use of computational journalism to assist with social media newsgathering. This review is relevant to computer scientists researching news in social media as well as for interdisciplinary researchers interested in the intersection of computer science and journalism.

show abstract

Media REVEALr: A Social Multimedia Monitoring and Intelligence System for Web Multimedia Verification

Cited by 3 publications

References 10 publications

Supporting the Use of User Generated Content in Journalistic Practice

Supporting the Use of User Generated Content in Journalistic Practice

Detection and Resolution of Rumours in Social Media

Mining social media for newsgathering: A review

Contact Info

Product

Resources

About