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.
|Number of pages||10|
|Journal||IEEE Signal Processing Magazine|
|Publication status||Published - 2012 Mar|
Bibliographical noteFunding 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