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Tagging Systems

Tagging systems help improve search, spam detection, reputation systems, personal organization, and metadata usage. This article explores different tagging systems and their design and attributes, as well as user incentives and case studies.

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Tagging Systems

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  1. Tagging Systems Mustafa Kilavuz

  2. Tags • A tag is a keyword added to an internet resource (web page, image, video) by users without relying on a controlled vocabulary. • Helps to improve search, spam detection, reputation systems, personal organization and metadata

  3. Usage • Social bookmarking • Personal bookmarks • Allows users to store and retrieve resources • Social tagging systems • Shared tags for particular resources • Each tag is a link to additional resources tagged the same way by other users • Folksonomy: popular tags

  4. Examples of Tagging Systems • Flickr: A photo sharing system allowing users to store and tag their personal photos, as well as maintain a network of contacts and tag others photos. • Del.icio.us: A “social bookmarking site,” allowing users to save and tag web pages and resources. • CiteULike: A site allowing users to tag citations and references, e.g. academic papers or books. • Youtube: A video sharing system allowing users to upload video content and describe it with tags. • ESP Game: An internet game of tagging where users are randomly paired with each other, and try to guess tags the other would use when presented with a random photo. • Last.fm: A music information database allowing members to tag artists, albums, and songs

  5. A model

  6. Vocabulary Problem • Different users use different terms to describe the same things • Polysemy: A single word has multiple meanings • Synonymy:Different words have the same meaning • Abstraction: Tagging a resource in different levels of abstraction • Animal, cat, Persian cat, Felissilvestriscatus longhair Persian • Different languages • Missing context: Tags that could not be related with the images by others • Holiday, me, friends, a person’s name

  7. Taxonomy of Tagging Systems • System design and attributes • How the characteristics of a tagging system effects the content, the tags and the usage • User incentives • How user incentives and motivations effect the content, the tags and the usage

  8. System Design and Attributes • Tagging rights: A tag can be added or removed by the creator of the resource, a restricted group or everyone • Tagging support: The mechanism of a tag entry • Blind tagging: a tagging user cannot see tags added by others to the same resource • Viewable tagging: all tags are visible • Suggestive tagging: the system suggests the user possible tags • Aggregation: Systems allow duplicate tagging (bag-model) or prevent (set-model) • Type of object: web pages, images, videos, songs • Source of Material: Resources can be supplied by the system or the users, or anything on the web can be tagged • Resource connectivity: links, groups etc. connecting resources other than tags • Social connectivity: The connection between the users may result localized folksonomies.

  9. User Incentives • Future retrieval: To mark items for personal retrieval of either the individual resource or a collection (playlists) • Contribution and sharing: To add to conceptual clusters for the value of either known or unknown audiences • Attract attention: to attract other users to look at their resources (common tags, spam tags) • Play and competition: to produce tags based on an internal or external set of rules • Self presentation: to write a user’s own identity lo leave a mark • Opinion expression: to convey value judgments that they wish to share with others

  10. Case Study: Flickr • Flickr is a photo-sharing site that considers tags as a core element to the sharing, retrieval, navigation and discovery of user-contributed images. • It allows users to upload their photos and share with the public. • People can create networks, join groups, send messages, comment, tag, choose favorite, explore etc. • It contains user-contributed resources instead of global resources. • It allows self-tagging (or permission-based) instead of free-for-all tagging. • The tags are aggregated in sets instead of bags. • It affords blind-tagging instead of suggested-tagging • This system design motivates people to tag.

  11. Tag Usage • The tag usage is not mandatory in Flickr. • User can tag their friends’ photos. But within 58 million tag observed, the overwhelming majority are owner tags. • Most people has very few distinct tags while a small group has extremely large sets of tags.

  12. Tag vocabulary size across the set of users

  13. Usefulness and importance of tags • The number of uploaded photos • The count of user’s distinct tags • The number of contacts designated by the user Might suggest that tagging is related to social activity to some degree A linear relation between the photos and the tags Flickr usage correlation

  14. Growth of distinct tags • 10 users are randomly chosen • Frequent uploaders ( > 100 photos) • Frequent taggers ( > 100 tags) • The number of distinct tags are observed as the number of photos uploaded increases.

  15. Growth of distinct tags

  16. Vocabulary Formation • Flickr allows social networks and interest groups. • There is a huge potential for social influence in the development of tag vocabularies. • People can follow updates from their contacts and this promotes constant tagging. • Randomly chosen 2500 people (frequent taggers) are paired with a random contact and a random user.

  17. Vocabulary Formation Vocabulary overlap distribution for random users and contacts

  18. Tag Categories

  19. Tag Categories

  20. Tag Categories

  21. Tag frequency distribution in Flickr

  22. Number of tags per photo in Flickr

  23. Conclusion • Social tagging systems have the potential to improve many information systems problems. • In order to study these system, the systems place in the taxonomy of architectures should be observed. • Different applications have different tagging systems and user motivations. • Tagging systems could be improved • Preventing problems of meaning • Finding relations between the tags (synonyms, abstractions) • Gathering information from the images

  24. QUESTIONS?

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