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This article delves into the intricate world of tagging systems, particularly on platforms like Flickr. It examines how users employ tags for personal organization and resource retrieval, facilitating social bookmarking and enhanced searchability. We explore key concepts such as folksonomy, polysemy, and synonymy, highlighting the discrepancies between user tagging practices. The analysis includes observations on tag frequency, distinct vocabulary growth, and spatial-temporal distribution of tags, offering insights into user interactions within social tagging systems.
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Flickr Tags Network Mustafa Kilavuz
Tags • A tag is a keyword • Search, spam detection, reputation systems, personal organization and metadata
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
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
Tag Usage on Flickr • 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.
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.
Vocabulary Formation Vocabulary overlap distribution for random users and contacts
Information from a Flickr Photo • Useful • User • Upload date • Tags • View count • Groups • Other • Title • Description • Comments
Data Extraction Method • http://www.flickr.com/services/api/ • Iterate on each user • Iterate on each public photo of the user • Get all contacts and group information • Iterate on each group • Iterate on each photo in the group
Tripartite Network Model page person tag • Can project onto bipartite graphs • person – tag • tag – page • person – page • Can project onto one-mode graphs • person – person • tag – tag • person - page From LadaAdamic
Tag Network Model Sky Building 34 12 10 Window Cloud 80 2 54 28 Blue 4 23 Light
Geotags • Additional place information • Spatial distribution, places • San Francisco, museum • Temporal distribution, events • Burst of tagging in a small time interval
Clusters • Strongly connected tags • Flickr uses for years for searching
What we can do.. • Investigate clusters to get information about • Events and their relations • Interests and their relations • Etc.
