This document summarizes a study comparing tags for social bookmarking (Wikipedia articles) versus social people tagging (friends on blog sites). It finds that tags for people are more subjective than tags for articles. Top tags for articles focused on their topic or category (e.g. history, politics), while top tags for friends included qualities like nice, funny. It also examined differences based on age and gender, finding tags could predict age but not gender of the tagged person or resource. The study aims to help design tag recommendation and information filtering systems for people tagging.

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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

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Bookmark-tagging and People-tagging
todo nlp
friendly
music
research
technician

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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 mechanismsAccess 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 ofHow 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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subjective
objective
uncategorized
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 potentiallytags will need to handle more potentially
unreliable tags
Controlled vocabularies?
Future work: Twitter Lists as person
annotations for information filtering