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Social People-Tagging vs.Social Bookmark-Tagging
Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
Peyman Nasirifard, Sheila Kinsella, Krystian Samp,
Stefan Decker
Digital Enterprise Research Institute www.deri.ie
Bookmark-tagging and People-tagging
todo nlpfriendly
music
researchtechnician
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Motivation
� Understand better how people tag each other
� A starting point for tag recommendation in frameworks based on people-tagging
� Access control mechanisms� Access control mechanisms
� Information filtering mechanisms
� We are especially interested in subjectivity of tags
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Main questions
� How do tags differ for resources of different categories? (person, event, country and city)
� How do tags for Wikipedia pages about persons differ from tags for friends?
� How do tags differ with age, gender of � How do tags differ with age, gender of taggee?
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Data collection
1. Bookmark tags
� Wikipedia articles: Person, Event, Country, City
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Data collection
2. People tags
� http://blog.* network of blog sites
� .ca, .co.uk, .de, .fr
� Google Translate to convert non-English to English
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Dataset
Source Category # Items # Tags # Unique
Wikipedia Person 4,031 75,548 14,346
Event 1,427 8,924 2,582
Country 638 13,002 3,200
City 1,137 4,703 1,907City 1,137 4,703 1,907
Blog sites Friend 2,927 17,126 10,913
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Person Event Country City
wikipedia history wikipedia travel
people war history wikipedia
philosophy wikipedia travel italy
history ww2 geography germany
wiki politics africa history
Top tags – Wikipedia articles
wiki politics africa history
music wiki culture london
politics military wiki uk
art battle reference wiki
books wwii europe places
literature iraq country england
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.de .fr .ca & .co.uk
music junkie art funny
nice politics music
live music life
funny kind kk friend
dear adorable funky
Top tags – blog sites
dear adorable funky
intelligent love friendly
pretty nice lovely
sexy drawing cool
love friendship sexy
honest trustworthy love
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Distribution of tags
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� Top 100 tags for each category
� 25 annotators each categorised 100 tags
� Objective e.g. “london”
� Subjective e.g. “jealous”
� Uncategorised e.g. “abcxyz”
Subjectivity of tags
� Uncategorised e.g. “abcxyz”
� Average inter-annotator agreement: 86%
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subjectiveobjectiveuncategorized
Friend Person Country City Event
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Randomly selected tags
� Before we looked at top tags, but what about long-tail tags?
� We also asked annotators to categorise 100 randomly chosen tags from each group
� Much higher rate of uncategorised (~3x)� Much higher rate of uncategorised (~3x)
� Lower inter-annotator agreement (76%)
� Less clear a meaning than the top tags, so probably less useful for applications like information filtering
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Linguistic categories
� Automatic classification (WordNet)
� Noun/verb/adjective/adverb/uncategorised
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Adjective Adverb Verb Noun Uncategorised
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Age and gender of taggees
� Generated sets of tags corresponding to ages brackets and genders
� Removed tags that refer to a specific gender
� Asked 10 participants if they could predict age and genderage and gender
� Results:
� Differences between gender were not perceptible
� Differences between younger and older were perceptible (and younger were more subjective)
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Conclusions
� Subjectivity: Articles of different categories are tagged similarly, but friends are assigned subjective tags more frequently
� Consequence: frameworks built on person-tags will need to handle more potentially tags will need to handle more potentially unreliable tags
� Controlled vocabularies?
� Future work: Twitter Lists as person annotations for information filtering