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Social People-Tagging vs. Social Bookmark-Tagging

Social People-Tagging vs. Social Bookmark-Tagging. Peyman Nasirifard, Sheila Kinsella , Krystian Samp, Stefan Decker. Bookmark-tagging and People-tagging. todo. nlp. friendly. music. research. technician. Motivation. Understand better how people tag each other

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Social People-Tagging vs. Social Bookmark-Tagging

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  1. Social People-Tagging vs.Social Bookmark-Tagging Peyman Nasirifard, Sheila Kinsella, Krystian Samp, Stefan Decker

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

  3. 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. 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. Data collection • Bookmark tags • Wikipedia articles: Person, Event, Country, City

  6. Data collection • People tags • http://blog.* network of blog sites • .ca, .co.uk, .de, .fr • Google Translate to convert non-English to English

  7. Dataset

  8. Top tags – Wikipedia articles

  9. Top tags – blog sites

  10. Distribution of tags

  11. Subjectivity 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%

  12. subjective objective uncategorized Friend Person Country City Event

  13. 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. Linguistic categories • Automatic classification (WordNet) • Noun/verb/adjective/adverb/uncategorised

  15. Adjective Adverb Verb Noun Uncategorised

  16. 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. 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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