Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: Non-collaborative Content Detecting On Video Sharing Social Networks
Author: Da Luz A.
Valle E.
Araujo A.D.A.
Abstract: In this work we are concerned with detecting non-collaborative videos in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting ballot stuffing and spam videos in threads of video responses. That is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information; and, which is paramount, of the context-dependent nature of non-collaborative videos. Content filtering for social networks is an increasingly demanded task: due to their popularity, the number of abuses also tends to increase, annoying the user and disrupting their services. We propose two approaches, each one better adapted to a specific non-collaborative action: ballot stuffing, which tries to inflate the popularity of a given video by giving "fake" responses to it, and spamming, which tries to insert a non-related video as a response in popular videos. We endorse the use of low-level features combined into higher-level features representation, like bag-of-visual-features and latent semantic analysis. Our experiments show the feasibility of the proposed approaches. © 2012 Springer Science+Business Media, LLC.
Editor: Kluwer Academic Publishers
Rights: fechado
Identifier DOI: 10.1007/s11042-012-1198-6
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-84901988189.pdf862.52 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.