Communications of the ACM
Carnegie Mellon Analysis Shows Online Songwriters Seek Collaborators With Complementary Skills, Status
Carnegie Mellon University researchers found that creative collaborators trying to expand their horizons seek out collaborators with complementary interests and skills.
Credit: Carnegie Mellon University
Carnegie Mellon University (CMU) researchers have found a way of determining the unique balance of qualities that contribute to musical collaboration using a path-based regression technique with implications for further social science research involving big data.
The group collected data over a four-year period from an online songwriting initiative called February Album Writing Month (FAWM), which challenges participants to compose 14 new works of music during the month of February.
Using a path-based regression program recently developed through a project called Never-Ending Language Learning, the researchers analyzed 39,103 songs, 6,116 participants, song tags, locations, and forums. Taking "random walks" through the FAWM data set, the program analyzed paths between potential collaborators to assess which paths were predictive and eliminate those that were not.
"With this technique, the program can randomly sample thousands of paths and automatically identify the ones that seem most noteworthy," says CMU researcher Burr Settles.
Participants were more likely to collaborate with those from different genres than those who shared their interests and skills. In addition, participants with equal social status in the community did not collaborate as often as those from disparate backgrounds.
From Carnegie Mellon News (PA)
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