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Content Tags Definition
A content tag is a term or keyword attached to web content that identifies characteristics of the content.
A Little More on What are Content Tags
Web content is, by default, categorized chronologically based upon when the content was posted to the website.
Content tags allow the web administrator to categorize web content into groups based upon similarities between the content.
Each article containing a specific content tag is categorically linked to other articles containing that same tag.
Even More of an Explanation of Content Tags
Content tags are often confused with Keyword tags and Category tags.
A keyword tag is a meta tag (or metadata) attached to specific words in a web post or page. The meta tag demonstrates the relevance or importance of the tagged words. For example, a meta tag may make words into a Header, Bolded, Underlined, or Hyperlinked to other content.
A category tag groups web posts into a categorical subject-matter. Basically, the web administrative identifies a category and categorizes all content that fits into that category. It differs from the content tag. While categories are subgroups of similar contents put together, tags are keywords assigned or attached to specific content. A category may comprise many topics and content, tags are assigned to each of the content items.
Using a network analysis approach for organizing social bookmarking tags and enabling web content discovery, Wei, W., & Ram, S. (2012). Using a network analysis approach for organizing social bookmarking tags and enabling web content discovery. ACM Transactions on Management Information Systems (TMIS), 3(3), 15.
Connecting content to community in social media via image content, user tags and user communication, De Choudhury, M., Sundaram, H., Lin, Y. R., John, A., & Seligmann, D. D. (2009, June). Connecting content to community in social media via image content, user tags and user communication. In Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on (pp. 1238-1241). IEEE. In this paper we develop a recommendation framework to connect image content with communities in online social media. The problem is important because users are looking for useful feedback on their uploaded content, but finding the right community for feedback is challenging for the end user. Social media are characterized by both content and community. Hence, in our approach, we characterize images through three types of features: visual features, user generated text tags, and social interaction (user communication history in the form of comments). A recommendation framework based on learning a latent space representation of the groups is developed to recommend the most likely groups for a given image. The model was tested on a large corpus of Flickr images comprising 15,689 images. Our method outperforms the baseline method, with a mean precision 0.62 and mean recall 0.69. Importantly, we show that fusing image content, text tags with social interaction features outperforms the case of only using image content or tags.
Integrating tags in a semantic content-based recommender, De Gemmis, M., Lops, P., Semeraro, G., & Basile, P. (2008, October). Integrating tags in a semantic content-based recommender. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 163-170). ACM.
Classifying tags using open content resources, Overell, S., Sigurbjörnsson, B., & Van Zwol, R. (2009, February). Classifying tags using open content resources. In Proceedings of the Second ACM International Conference on Web Search and Data Mining (pp. 64-73). ACM. Tagging has emerged as a popular means to annotate on-line objects such as bookmarks, photos and videos. Tags vary in semantic meaning and can describe different aspects of a media object. Tags describe the content of the media as well as locations, dates, people and other associated meta-data. Being able to automatically classify tags into semantic categories allows us to understand better the way users annotate media objects and to build tools for viewing and browsing the media objects. In this paper we present a generic method for classifying tags using third party open content resources, such as Wikipedia and the Open Directory. Our method uses structural patterns that can be extracted from resource meta-data. We describe the implementation of our method on Wikipedia using WordNet categories as our classification schema and ground truth. Two structural patterns found in Wikipedia are used for training and classification: categories and templates. We apply our system to classifying Flickr tags. Compared to a WordNet baseline our method increases the coverage of the Flickr vocabulary by 115%. We can classify many important entities that are not covered by WordNet, such as, London Eye, Big Island, Ronaldinho, geocaching and wii.
Tags are not metadata, but” just more content“-to some people., Berendt, B., & Hanser, C. (2007, March). Tags are not metadata, but” just more content”-to some people. In ICWSM.
Can social bookmarking enhance search in the web?, Yanbe, Y., Jatowt, A., Nakamura, S., & Tanaka, K. (2007, June). Can social bookmarking enhance search in the web?. In Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries (pp. 107-116). ACM.
In tags we trust: Trust modeling in social tagging of multimedia content, Ivanov, I., Vajda, P., Lee, J. S., & Ebrahimi, T. (2012). In tags we trust: Trust modeling in social tagging of multimedia content. IEEE Signal Processing Magazine, 29(2), 98-107. 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.
Usefulness of tags in providing access to large information systems, Melenhorst, M., & van Setten, M. (2007, October). Usefulness of tags in providing access to large information systems. In Professional communication conference, 2007. ipcc 2007. ieee international (pp. 1-9). IEEE.
Quest for relevant tags using local interaction networks and visual content, Sawant, N., Datta, R., Li, J., & Wang, J. Z. (2010, March). Quest for relevant tags using local interaction networks and visual content. In Proceedings of the international conference on Multimedia information retrieval (pp. 231-240). ACM.
Recommending tags for pictures based on text, visual content and user context, Lindstaedt, S., Pammer, V., Mörzinger, R., Kern, R., Mülner, H., & Wagner, C. (2008, June). Recommending tags for pictures based on text, visual content and user context. In Internet and Web Applications and Services, 2008. ICIW’08. Third International Conference on (pp. 506-511). IEEE. Imagine you are member of an online social system and want to upload a picture into the community pool. In current social software systems, you can probably tag your photo, share it or send it to a photo printing service and multiple other stuff. The system creates around you a space full of pictures, other interesting content (descriptions, comments) and full of users as well. The one thing current systems do not do, is understand what your pictures are about. We present here a collection of functionalities that make a step in that direction when put together to be consumed by a tag recommendation system for pictures. We use the data richness inherent in social online environments for recommending tags by analysing different aspects of the same data (text, visual content and user context). We also give an assessment of the quality of thus recommended tags.
Can all tags be used for search?, Bischoff, K., Firan, C. S., Nejdl, W., & Paiu, R. (2008, October). Can all tags be used for search?. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 193-202). ACM.