Social People-Tagging vs. Social Bookmark-Tagging


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EKAW 2010 paper by Peyman Nasirifard, Sheila Kinsella, Krystian Samp, and Stefan Decker

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Social People-Tagging vs. Social Bookmark-Tagging

  1. 1. Digital Enterprise Research Institute Social People-Tagging vs. Social Bookmark-Tagging  Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Peyman Nasirifard, Sheila Kinsella, Krystian Samp, Stefan Decker
  2. 2. Digital Enterprise Research Institute Bookmark-tagging and People-tagging todo nlp friendly music research technician
  3. 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 mechanismsAccess control mechanisms Information filtering mechanisms We are especially interested in subjectivity of tags
  4. 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 ofHow do tags differ with age, gender of taggee?
  5. 5. Digital Enterprise Research Institute Data collection 1. Bookmark tags Wikipedia articles: Person, Event, Country, City
  6. 6. Digital Enterprise Research Institute Data collection 2. People tags http://blog.* network of blog sites .ca,, .de, .fr Google Translate to convert non-English to English
  7. 7. Digital Enterprise Research Institute 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
  8. 8. Digital Enterprise Research Institute 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
  9. 9. Digital Enterprise Research Institute .de .fr .ca & 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
  10. 10. Digital Enterprise Research Institute Distribution of tags
  11. 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” Subjectivity of tags Uncategorised e.g. “abcxyz” Average inter-annotator agreement: 86%
  12. 12. Digital Enterprise Research Institute subjective objective uncategorized Friend Person Country City Event
  13. 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)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. 14. Digital Enterprise Research Institute Linguistic categories Automatic classification (WordNet) Noun/verb/adjective/adverb/uncategorised
  15. 15. Digital Enterprise Research Institute Adjective Adverb Verb Noun Uncategorised
  16. 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 genderage and gender Results: Differences between gender were not perceptible Differences between younger and older were perceptible (and younger were more subjective)
  17. 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 potentiallytags will need to handle more potentially unreliable tags Controlled vocabularies? Future work: Twitter Lists as person annotations for information filtering