Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise...

Copyright 2009 Digital Enterprise Research Institute. All rights reserved.

Digital Enterprise Research Institute www.deri.ie

Social People-Tagging vs.Social Bookmark-Tagging

Peyman Nasirifard, Sheila Kinsella, Krystian Samp, Stefan Decker


Bookmark-tagging and People-tagging

todo

research

nlp

technician

friendly

music


Motivation

Understand better how people tag each other

A starting point for tag recommendation in frameworks based on people-tagging Access control mechanisms Information filtering mechanisms

We are especially interested in subjectivity of tags


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 taggee?


Data collection

1. Bookmark tags Wikipedia articles: Person, Event, Country,

City


Data collection

2. People tags http://blog.* network of blog sites

.ca, .co.uk, .de, .fr

Google Translate to convert non-English to English


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 1,907

Blog sites Friend 2,927 17,126 10,913


Person Event Country City

wikipedia history wikipedia travel

people war history wikipedia

philosophy wikipedia travel italy

history ww2 geography germany

wiki politics africa history

music wiki culture london

politics military wiki uk

art battle reference wiki

books wwii europe places

literature iraq country england

Top tags – Wikipedia articles


.de .fr .ca & .co.uk

music junkie art funny

nice politics music

live music life

funny kind kk friend

dear adorable funky

intelligent love friendly

pretty nice lovely

sexy drawing cool

love friendship sexy

honest trustworthy love

Top tags – blog sites


Distribution of tags


Top 100 tags for each category 25 annotators each categorised 100 tags

Objective e.g. “london” Subjective e.g. “jealous” Uncategorised e.g. “abcxyz”

Average inter-annotator agreement: 86%

Subjectivity of tags


Friend Person Country City Event

subjectiveobjectiveuncategorized


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) Lower inter-annotator agreement (76%) Less clear a meaning than the top tags, so

probably less useful for applications like information filtering


Linguistic categories

Automatic classification (WordNet) Noun/verb/adjective/adverb/uncategorised


Adjective Adverb Verb Noun Uncategorised


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 gender

Results: Differences between gender were not

perceptible Differences between younger and older were

perceptible (and younger were more subjective)


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 unreliable tags Controlled vocabularies?

Future work: Twitter Lists as person annotations for information filtering

Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise...

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