publication . Article . Other literature type . 2019

A corpus of debunked and verified user-generated videos

Papadopoulou, Olga; Zampoglou, Markos; Papadopoulos, Symeon; Kompatsiaris, Ioannis;
Open Access English
  • Published: 11 Feb 2019 Journal: Online Information Review, volume 43, issue 1, pages 72-88 (issn: 1468-4527, Copyright policy)
Abstract
<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>As user-generated content (UGC) is entering the news cycle alongside content captured by news professionals, it is important to detect misleading content as early as possible and avoid disseminating it. The purpose of this paper is to present an annotated dataset of 380 user-generated videos (UGVs), 200 debunked and 180 verified, along with 5,195 near-duplicate reposted versions of them, and a set of automatic verification experiments aimed to serve as a baseline for future comparisons.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/metho...
Persistent Identifiers
Subjects
free text keywords: Library and Information Sciences, Information Systems, Computer Science Applications, Video verification, Fake news, Disinformation detection, User-generated content, Social media, Dataset, Social media, Systematic process, Manual annotation, Full text search, Information retrieval, Dissemination, User-generated content, Existential quantification, Media literacy, Computer science
Funded by
EC| InVID
Project
InVID
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
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