In tags we trust: Trust modeling in social tagging of multimedia content

Ivan Ivanov; Peter Vajda; Jong Seok Lee; Touradj Ebrahimi

doi:10.1109/MSP.2011.942345

In tags we trust: Trust modeling in social tagging of multimedia content

Ivan Ivanov, Peter Vajda, Jong Seok Lee, Touradj Ebrahimi

Research output: Contribution to journal › Review article › peer-review

19 Citations (Scopus)

Abstract

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.

Original language	English
Article number	6153150
Pages (from-to)	98-107
Number of pages	10
Journal	IEEE Signal Processing Magazine
Volume	29
Issue number	2
DOIs	https://doi.org/10.1109/MSP.2011.942345
Publication status	Published - 2012 Mar

Bibliographical note

Funding Information:
This work was supported by the Swiss National Foundation for Scientific Research in t he framework of NCCR Interactive Multimodal Information Management (IM2), the Swiss National Science Foundation Grant “Multimedia Security” (2 00020-113709), and partially supported by the European Network of Excellence PetaMedia (FP7/2007-2011).

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/MSP.2011.942345

Cite this

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abstract = "Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.",

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note = "Funding Information: This work was supported by the Swiss National Foundation for Scientific Research in t he framework of NCCR Interactive Multimodal Information Management (IM2), the Swiss National Science Foundation Grant “Multimedia Security” (2 00020-113709), and partially supported by the European Network of Excellence PetaMedia (FP7/2007-2011).",

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AU - Ebrahimi, Touradj

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In tags we trust: Trust modeling in social tagging of multimedia content

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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