publication . Article . Other literature type . 2019

A corpus of debunked and verified user-generated videos

Olga Papadopoulou; Markos Zampoglou; Symeon Papadopoulos; Ioannis Kompatsiaris;
Open Access
  • Published: 11 Feb 2019
Abstract
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.,The dataset was formed using a systematic process combining text search and near-duplicate video retrieval, followed by manual annotation using a set of journalism-inspired guide...
Subjects
free text keywords: Video verification, Fake news, Disinformation detection, User-generated content, Social media, Dataset, Library and Information Sciences, Information Systems, Computer Science Applications, Systematic process, Video retrieval, Information retrieval, Manual annotation, Dissemination, Computer science, Existential quantification, Full text search
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
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue
publication . Article . Other literature type . 2019

A corpus of debunked and verified user-generated videos

Olga Papadopoulou; Markos Zampoglou; Symeon Papadopoulos; Ioannis Kompatsiaris;