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 Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute www.deri.ie Social People-Tagging vs.

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Presentation on theme: " Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute www.deri.ie Social People-Tagging vs."— Presentation transcript:

1  Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Social People-Tagging vs. Social Bookmark-Tagging Peyman Nasirifard, Sheila Kinsella, Krystian Samp, Stefan Decker

2 Digital Enterprise Research Institute Bookmark-tagging and People-tagging todo research nlp technician friendly music

3 Digital Enterprise Research Institute 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

4 Digital Enterprise Research Institute 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?

5 Digital Enterprise Research Institute Data collection 1. Bookmark tags  Wikipedia articles: Person, Event, Country, City

6 Digital Enterprise Research Institute Data collection 2. People tags  network of blog sites .ca,.co.uk,.de,.fr  Google Translate to convert non-English to English

7 Digital Enterprise Research Institute Dataset SourceCategory # Items # Tags# Unique WikipediaPerson 4,03175,54814,346 Event 1,4278,9242,582 Country 63813,0023,200 City 1,1374,7031,907 Blog sitesFriend 2,92717,12610,913

8 Digital Enterprise Research Institute PersonEventCountryCity wikipediahistorywikipediatravel peoplewarhistorywikipedia philosophywikipediatravelitaly historyww2geographygermany wikipoliticsafricahistory musicwikiculturelondon politicsmilitarywikiuk artbattlereferencewiki bookswwiieuropeplaces literatureiraqcountryengland Top tags – Wikipedia articles

9 Digital Enterprise Research Institute &.co.uk music junkieartfunny nicepoliticsmusic livemusiclife funnykindkk friend dearadorablefunky intelligentlovefriendly prettynicelovely sexydrawingcool lovefriendshipsexy honesttrustworthylove Top tags – blog sites

10 Digital Enterprise Research Institute Distribution of tags

11 Digital Enterprise Research Institute 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

12 Digital Enterprise Research Institute Friend Person Country City Event subjective objective uncategorized

13 Digital Enterprise Research Institute 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

14 Digital Enterprise Research Institute Linguistic categories Automatic classification (WordNet) Noun/verb/adjective/adverb/uncategorised

15 Digital Enterprise Research Institute Adjective Adverb Verb Noun Uncategorised

16 Digital Enterprise Research Institute 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)

17 Digital Enterprise Research Institute 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


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