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.
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.
CONCLUSION:
In this article, we
dealt with one of the key issues in social tagging systems:
combatting noise and spam. We classified existing studies in the literature into
two categories, i.e., content and user trust modeling. Representative
techniques in each category were analyzed and compared. In addition, existing
databases and evaluation protocols were re viewed. An example system was
presented to demonstrate how trust modeling can be particularly employed in
a popular application of image sharing and geotagging. Finally, open issues and
future research trends were prospected. As online social networks and content
sharing services evolve rapidly, we believe that the research on enhancing
reliability and trustworthiness of such services will become increasingly
important.
combatting noise and spam. We classified existing studies in the literature into
two categories, i.e., content and user trust modeling. Representative
techniques in each category were analyzed and compared. In addition, existing
databases and evaluation protocols were re viewed. An example system was
presented to demonstrate how trust modeling can be particularly employed in
a popular application of image sharing and geotagging. Finally, open issues and
future research trends were prospected. As online social networks and content
sharing services evolve rapidly, we believe that the research on enhancing
reliability and trustworthiness of such services will become increasingly
important.
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