Music preference has been related to individual differences like social identity, cognitive style, and personality, but quantifying music preference can be a challenge. Self-report measures may be too presumptive of shared genre definitions between listeners, while listener ratings of expert-selected music may fail to reflect typical listeners’ genre boundaries. The current study aims to address this by using a social-tagging approach to select music for studying preference. In this study, 2,407 tracks were collected and subsampled from the Last.fm social-tagging service and the EchoNest platform based on attributes such as genre, tempo, and danceability. The set was further subsampled according to tempo estimates and metadata from EchoNest, resulting in 48 excerpts from 12 genres. Participants (n = 210) heard and rated the excerpts, rated each genre using the Short Test of Music Preferences (STOMP; n.d.), and completed the Ten-Item Personality Index (TIPI), the Empathy Quotient (EQ) and the Systemizing Quotient (SQ). Mean preference ratings correlated significantly with STOMP scores, suggesting that social tagging can provide a fairly reliable link between perception and genre labels. Principal component analysis (PCA) of the ratings revealed 4 musical components: “Danceable,” “Jazzy,” “Hard,” and “Rebellious.” Component scores correlated modestly but significantly with TIPI, EQ, and SQ scores. These results support and expand previous findings linking personality and music preference, and provide support for a novel method of using crowd tagging in the study of music preference. (PsycINFO Database Record (c) 2017 APA, all rights reserved)