Music, especially on a track-by-track level creates unique and challenging problems for both catalogers and users. Many libraries utilize terminology from The Library of Congress Subject Headings (LCSH) when cataloging their collections. However, the use of LCSH for cataloging music in a modern library can leave both catalogers and users working around large deficits. The problems with using LCSH stem primarily from its limited vocabulary for music. Due to the focus of the Library of Congress, the LCSH vocabulary consists mainly of terms for Western Classical music and generally lacks in vocabulary for non-Western music or popular music [Gaffney, M. (2008) p.14]. Even searching for pieces that fall underneath the umbrella of LCSH’s focus can be hard for users to locate, as authority records utilize the original name of a piece in its original language. European Classical music can be particularly confusing and often frustrating for users who only know the titles in English. Furthermore, outside of the lack of user friendly vocabulary for searching for artists, genres and tracks there is also the larger problem of how today’s patrons are accustomed to searching for music or discovering new tracks.
Although physical manifestations of music have seemingly clear entry points – a CD’s labeling and accompanying liner notes – the ways that many patrons now expect to discover music has little to do with this external information. Much like how a customer might browse the shelves of a bookstore or page through an online retailer for a new novel, users expect to be able to search for music with keywords related to specific genres, locations, artists and even moods. The creation of this data requires either a round-the-clock team of highly trained experts or alternatively the input of the crowd.
Two popular music folksonomies with active user bases are Last.fm and MusicBrainz. Other popular music recommender sites like Pandora track user input in order to tailor their recommendations, but rely on their own paid professionals to rate and assign tags to each track. Conversely, Last.fm is powered by its users who are encouraged to tag however they see fit using whatever language or style of tagging they prefer. MusicBrainz, falls somewhere between these two other sites. Although it is still a folksonomy, users adhere to a list of community standards for tagging.
Many of the most popular music folksonomies are wrapped around user services such as social communities (Last.fm and MusicBrainz) or music recommenders (Rdio). These services are important because music folksonomies rely on users to tag content. Users participate in the folksonomy for a variety of reasons, many of which are connected to the social or service aspects of these websites. Music folksonomies offer music listeners the opportunity to learn more about their music listening habits, socially connect with other music lovers and organize their personal music collections. In turn, all of these actions also help to create important tag data that is useful to the broader community within the folksonomy, and that can also be harnessed to make library search more robust and accurate.
Last.fm also gives users the option to download a software program – Scrobbler – that communicates their music listening habits from their personal computer to Last.fm. Last.fm then either utilizes the personal genre tags that they have applied to their MP3 files, or users can choose to modify these tags on the website. Due to the large and often daily amount listening information many users share with Last.fm, the site has created different ways for users to view and utilize the information collected from their listening habits. A popular tool created by the Last.fm Labs is ‘Mood Hacker’[1] which takes the mood tags associated with the songs that a user has listened to and creates a visualization that illustrates their mood through their listening history. Tools like ‘Mood Hacker’ are a great example of how to motivate users to tag regularly and accurately — a motivation that can all too easily be lost in the world of folksonomies.
The accuracy and the regularity of user participation are often the most criticized aspects of folksonomy users and the tags that they generate. The issue of the selfish tag versus the social tag resonates throughout all folksonomies but can be particularly tricky when applied to music folksonomies, as sites like Last.fm encourage users to tag in whatever manner is most meaningful for them. Rader and Wash [Rader (2006).] in their paper on social versus selfish tagging define much of what is considered to be the ‘noise’ in folksonomies to be selfish tags. Selfish tags are tags that are not explicitly helpful or meaningful to the broader community. These tags might be overly subjective (‘worst song ever,’ or ‘my favorite song in the world’), purely for personal classification or book-marking (‘seen live,’ ‘I own it,’ ‘check out’) or even some mood or opinion tagging that is purposefully obscure or descriptions that include a full phrase and are therefore not easily duplicated (‘space trucking,’ ‘all my hope is gone’).
The converse of the selfish tag is the social tag; social tags are beneficial to a broader community. These tags can help to organize and label genres or trends that are otherwise not visible or not recognized by the music industry. For example on Last.fm a subgenre of rock is punk and within punk is the genre of horror punk which was built by 10,319 people and has been used as a tag 31,538[2]. Social tags can also include bibliographic information such as the artists involved in a tagged piece, the location of recording or place of origin, the year created and the instruments used in a specific track. Many mood tags are also social as they help to guide other users to finding new music from different genres but with similar ‘feeling.’
The argument can be made however, that many seemingly selfish tags are in fact social tags. Although truly selfish tags do add extra noise to a folksonomy and can drown out the message of social tags, selfish tags are not always as selfish as they may seem. What may at first appear selfish might actually be the definition of a new genre. Genres like Cuddlecore and NewWeirdAmerica are both defined on Last.fm’s wiki but could easily have been construed as selfish when they were emerging, do to their unusual names and the obscurity of their fan base. The world of music is moving at a much faster pace than it ever has before. Artists can record their own music at home and broadcast it out to potential fans for free using the internet. Music folksonomies can help to keep the vocabulary of music growing and evolving at the same tempo as music itself.
Music folksonomies can be important in the development of library catalogs and the organization of online music libraries. According to Hunter, benefits to libraries include “low entry cost; a simple, unstructured but relevant vocabulary that is broadly shared by user base; and the ability to adapt quickly to new terminology” [Hunter, J. (2009).]. Additionally the benefits for libraries with eclectic or very modern collections folksonomies can offer a vocabulary as defined by equally eclectic or modern users. Furthermore the redundancy that can often be negatively associated with user created tags can be beneficial to library search as these synonyms can create more pathways to each selection in the catalog.
Despite their benefits folksonomies still have many short comings. Even if all of the taggers participating in a folksonomy adhered to strict community rules, human error would still create faulty in tags such as misspellings or wrong identifications that would detract from the overall accuracy and utility of the vocabulary. Additionally there is generally a lack of hierarchical structure in folksonomies, which can hinder cataloging and organizing. However if users and catalogers keep in mind the short comings of folksonomies while pairing them with more traditional vocabularies, folksonomies can benefit music retrieval.
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Bello, J. P., & Underwood, K. (2012). Improving access to digital music through content-based analysis. OCLC Systems & Services, 28(1), 17-31.
Fields, K. (2007). Ontologies, categories, folksonomies: an organised language of sound. Organised Sound, 12(2), 101.
Gaffney, M. (2008). Independent music discovery, retrieval, and promotion via folksonomies and social networking sites.
Gaffney, M., & Rafferty, P. (2009). Making the long tail visible: social networking sites and independent music discovery. Program: electronic library and information systems, 43(4), 375-391.
Goh, D. H. L., Chua, A., Lee, C. S., & Razikin, K. (2009). Resource discovery through social tagging: a classification and content analytic approach. Online Information Review, 33(3), 568-583.
Haklae Kim, John Breslin, Jae Hwa Choi, (2010) “Semantic representation for copyright metadata of user-generated content in folksonomies”, Online Information Review, Vol. 34 Iss: 4, pp.626 – 641
Hunter, J. (2009), “Collaborative semantic tagging and annotation systems”, Annual Review of Information Science and Technology, Vol. 43, pp. 187-239.
Knowles, J. D. (2007). A survey of Web 2.0 music trends and some implications for tertiary music communities.
Levy, M., & Sandler, M. (2007). A semantic space for music derived from social tags. In Proceedings of the 8th International Society for Music Information Retrieval Conference (Vol. 1, p. 12).
Majors, R., & Mantz, S. L. (2011). Moving to the patron’s beat. OCLC Systems & Services, 27(4), 275-283.
Rader, E. and Wash, R. (2006), “Tagging with del.icio.us: social or selfish?”, paper presented at Computer Supported Cooperative Work 2006, November 4-8, Banff, Alberta, Canada, available at: http://bierdoctor.com/papers/Rader_CSCW_Abstract_Final.pdf (accessed 5 April 2009).
Santini, R. M. (2011). Collaborative classification of popular music on the internet and its social implications. OCLC Systems & Services, 27(3), 210-247.
Sordo, M., Celma, O., Blech, M., & Guaus, E. (2008). The quest for musical genres: Do the experts and the wisdom of crowds agree. Philadelphia, USA.
Thomas, B. (2011). Creating a specialized music search interface in a traditional OPAC environment. OCLC Systems & Services, 27(3), 248-256.
Thompson, A. E. (2008). Playing Tag: An Analysis of Vocabulary Patterns and Relationships Within a Popular Music Folksonomy. Master’s thesis, University of North Carolina at Chapel Hill.
[1] Last.fm Labs. ‘Mood Hacker.’ Retrieved from: http://playground.last.fm/demo/moody
[2] Horror Punk. Retrieved December 9, 2012 from: http://www.last.fm/tag/horror%20punk
