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Understanding What Users Need to Understand Us (and Our Data): Crowdsourcing Metadata

Understanding What Users Need to Understand Us (and Our Data): Crowdsourcing Metadata. Edward Benoit, III School of Library & Information Science, LSU. Previous Research & Focus. Tagging studies main focus on still images or text User-generated content as time saving mechanism

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Understanding What Users Need to Understand Us (and Our Data): Crowdsourcing Metadata

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  1. Understanding What Users Need to Understand Us (and Our Data): Crowdsourcing Metadata Edward Benoit, III School of Library & Information Science, LSU

  2. Previous Research & Focus • Tagging studies main focus on still images or text • User-generated content as time saving mechanism • Challenges and opportunities of moving images • Initial research question: What effect does online video length have on crowdsourced metadata/user-generated tags?

  3. Methodology • Mixed methods, quasi-experimental two-group design • 40 participants randomly assigned to 2 groups and tag: • One 30 minute video -OR- • Three 10 minute videos • Analysis: • Open coding • Descriptive statistics

  4. Sample Video(s):LDMA http://www.ladigitalmedia.org/

  5. Participants • Gender: 22.5% Male, 77.5%Female • Age: 19-65; 30.9 Mean • Location: 65% Louisiana • Race: • 77.5% White • 10% Black • 5% Hispanic • 5% Asian • 2.5% Other

  6. Number of Tags: Short Videos

  7. Sample Video(s):LDMA Statistically significant difference between the number of tags generated for the long video (M=16.1, SD=22.6) and the short videos (M=27.75, SD=12.1); t(38)=-3.11, p=0.004.

  8. Top Tags Long Video Short Videos Cajun Christmas (19) Louisiana (17) Christmas (16) Papa Noel (16) Cajun (12) Bayou (11) Bayou Parade (11) Cajun Night Before Christmas (10) Food (9) • Christmas (14) • Papa Noel (13) • cajun (10) • Louisiana (9) • Cajun Night Before Christmas (6) • Lafayette (6) • Natchitoches (6) • Bayou (5) • Cajun Christmas (5) • Fireworks (5)

  9. Metadata Comparison • Compiled list of metadata words • Post-stop list removal, 89 words • Compared with generated tags: • Full Video: 41.6% matching • Short Videos: 48.3% matching

  10. Main Types of Tags • Content description • Identification (persons, places, music) • Emotional responses

  11. Future Directions • Comparison of amateur and professional online videos • Tagging enticement techniques

  12. Thank you!Questions?

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