CROWDSOURCING METADATA FOR LIBRARY AND MUSEUM COLLECTIONS USING A TAXONOMY OF FLICKR USER BEHAVIOR A Thesis Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Master of Science by Evan Fay Earle January 2014 © 2014 Evan Fay Earle ABSTRACT Library and museum staff members are faced with having to create descriptions for large numbers of items found within collections. Immense collections and a shortage of staff time prevent the description of collections using metadata at the item level. Large collections of photographs may contain great scholarly and research value, but this information may only be found if items are described in detail. Without detailed descriptions, the items are much harder to find using standard web search techniques, which have become the norm for searching library and museum collection catalogs. To assist with metadata creation, institutions can attempt to reach out to the public and crowdsource descriptions. An example of crowdsourced description generation is the website, Flickr, where the entire user community can comment and add metadata information in the forms of tags to other users’ images. This paper discusses some of the problems with metadata creation and provides insight on ways in which crowdsourcing can benefit institutions. Through an analysis of tags and comments found on Flickr, behaviors are categorized to show a taxonomy of users. This information is used in conjunction with survey data in an effort to show if certain types of users have characteristics that are most beneficial to enhancing metadata in existing library and museum collections. BIOGRAPHICAL SKETCH Evan Fay Earle is a 2002 graduate of Cornell University with a Bachelors of Science. His studies spanned a broad array of subjects with concentrations in education, business, communication, and information science. As an entrepreneur and avid collector of rare and unique books, he gained a greater understanding of the importance of metadata in managing large collections of books and disseminating information about them. His interest in the subject expanded through his experience managing data at a startup dot-com during the internet bubble of the late 1990s. While the technical and theoretical aspects of metadata management were intriguing, it was combining these with his passion for education and learning involving libraries and museums which lead to a fruitful connection. While working with the Cornell University Laboratory of Ornithology, Evan completed a database of the over 1,000 pieces of artwork owned by the lab. The database, now available online with images of the artwork, contained over fifty different categories of metadata describing the artwork, its subjects, and its illustrators. Work with the Lab of Ornithology led to an appointment with Cornell’s Division of Rare and Manuscript Collections at Kroch Library as the Archival Technical Services Coordinator where, for nearly a decade, he has managed and participated in a wide assortment of digital projects, involving massive amounts of metadata from collections held by this world class library. His work has helped provide high quality, rich descriptions of some of the University’s most treasured collections for scholars all over the world. Research for Kroch Library has been in concert with his graduate studies. Several collections of digital materials have implemented crowdsourcing strategies that were developed over the course of his graduate work. Evan also consults on digital projects as the archivist for the International Motor Racing Research Center in Watkins Glen, New York, where he is applying information gleaned from his graduate studies. iii AKNOWLEDGEMENTS Completion of this degree was made through Cornell University’s Employee Degree Program, which, while not the easiest way to earn a degree, provided tuition assistance and allowed for studies while maintaining my work obligations as well as my paycheck. I am grateful to my colleagues in the Cornell University Library system for providing me the opportunity and flexibility to work towards the degree. Similarly, I am extremely thankful for the patience and guidance I received from the faculty within my graduate program for providing me with such a rich learning experience. Lastly this would not have been possible without the generous support of my family. iv TABLE OF CONTENTS ABSTRACT ................................................................................................................................................................................. BIOGRAPHICAL SKETCH ................................................................................................................................................. III AKNOWLEDGEMENTS ...................................................................................................................................................... IV INTRODUCTION ..................................................................................................................................................................... 6 MACHINE CREATED DIGITAL METADATA VERSUS HAND-CODED METADATA AND THE BACKLOG PROBLEM ............................ 6 CONTENT DISSEMINATORS AND USER GENERATED INFORMATION .......................................................................................... 9 IMPORTANCE OF NEEDING TO FIND EFFICIENT, ACCURATE METADATA CREATION............................................................... 10 EXPECTATIONS OF LIBRARIES FROM STAFF AND RESEARCHERS ........................................................................................... 12 LITERATURE REVIEW ....................................................................................................................................................... 13 QUANTITY OF TAGS ............................................................................................................................................................... 14 MOTIVATIONS OF TAGGERS .................................................................................................................................................. 16 TYPES OF TAGS ...................................................................................................................................................................... 17 TAG ACCURACY ..................................................................................................................................................................... 20 CONSISTENCY AND CONTROLLED VOCABULARY FOLKSONOMIES ......................................................................................... 21 METHODOLOGY .................................................................................................................................................................. 25 INTERVIEWS .......................................................................................................................................................................... 25 Interview Questions .......................................................................................................................................................... 27 TAGGING PERCEPTIONS AND PREFERENCES STUDY ............................................................................................................. 28 FLICKR TAGGING DATA ......................................................................................................................................................... 29 INTERVIEW ANALYSIS ...................................................................................................................................................... 35 RESPONSES REGARDING TAGGING MOTIVATIONS ................................................................................................................ 35 RESPONSES REGARDING TAGGING TYPES ............................................................................................................................. 37 RESPONSES REGARDING CONTROLLED VOCABULARIES ........................................................................................................ 38 RESPONSES REGARDING PERCEIVED ACCURACY .................................................................................................................. 39 LIBRARY VIEWS OF USER GENERATED CONTENT ................................................................................................................. 40 TAGGING PERCEPTIONS STUDY RESULTS ................................................................................................................. 42 FLICKER USER ANALYSIS AND TAXONOMY .............................................................................................................. 45 HOBBYISTS ............................................................................................................................................................................ 45 EXPERTS ................................................................................................................................................................................ 47 SPECIALISTS .......................................................................................................................................................................... 54 GEOCODERS .........................................................................................................................................................................