#MPLP: a Comparison of Domain Novice and Expert User-generated Tags in a Minimally Processed Digital Archive - (:unkn) unknown (My University, 2014)
The high costs of creating and maintaining digital archives precluded many archives from providing users with digital content or increasing the amount of digitized materials. Studies have shown users increasingly demand immediate online access to archival materials with detailed descriptions (access points). The adoption of minimal processing to digital archives limits the access points at the folder or series level rather than the item-level description users' desire. User-generated content such as tags, could supplement the minimally processed metadata, though users are reluctant to trust or use unmediated tags. This dissertation project explores the potential for controlling/mediating the supplemental metadata from user-generated tags through inclusion of only expert domain user-generated tags. The study was designed to answer three research questions with two parts each: 1(a) What are the similarities and differences between tags generated by expert and novice users in a minimally processed digital archive?, 1(b) Are there differences between expert and novice users' opinions of the tagging experience and tag creation considerations?, 2(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with metadata in a traditionally processed digital archive?, 2(b) Does user knowledge affect the proportion of tags matching unselected metadata in a minimally processed digital archive?, 3(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with existing users' search terms in a digital archive?, and 3(b) Does user knowledge affect the proportion of tags matching query terms in a minimally processed digital archive? The dissertation project was a mixed-methods, quasi-experimental design focused on tag generation within a sample minimally processed digital archive. The study used a sample collection of fifteen documents and fifteen photographs. Sixty participants divided into two groups (novices and experts) based on assessed prior knowledge of the sample collection's domain generated tags for fifteen documents and fifteen photographs (a minimum of one tag per object). Participants completed a pre-questionnaire identifying prior knowledge, and use of social tagging and archives. Additionally, participants provided their opinions regarding factors associated with tagging including the tagging experience and considerations while creating tags through structured and open-ended questions in a post-questionnaire. An open-coding analysis of the created tags developed a coding scheme of six major categories and six subcategories. Application of the coding scheme categorized all generated tags. Additional descriptive statistics summarized the number of tags created by each domain group (expert, novice) for all objects and divided by format (photograph, document). T-tests and Chi-square tests explored the associations (and associative strengths) between domain knowledge and the number of tags created or types of tags created for all objects and divided by format. The subsequent analysis compared the tags with the metadata from the existing collection not displayed within the sample collection participants used. Descriptive statistics summarized the proportion of tags matching unselected metadata and Chi-square tests analyzed the findings for associations with domain knowledge. Finally, the author extracted existing users' query terms from one month of server-log data and compared the generated-tags and unselected metadata. Descriptive statistics summarized the proportion of tags and unselected metadata matching query terms, and Chi-square tests analyzed the findings for associations with domain knowledge. Based on the findings, the author discussed the theoretical and practical implications of including social tags within a minimally processed digital archive.
Keywords: Archives; Digital Archives; Domain Knowledge; MPLP; Social Tagging; Web 2.0