Markus Schedl
ABSTRACT
Music is known to exhibit different characteristics, depending on genre and style. While most research that studies such differences takes a musicological perspective and analyzes acoustic properties of individual pieces or artists, we conduct a large-scale analysis using various web resources. Exploiting content information from song lyrics, contextual information reflected in music artists' Wikipedia articles, and listening information, we particularly study the aspects of popularity, length, repetitiveness, and readability of lyrics and Wikipedia articles. We measure popularity in terms of song play count (PC) and listener count (LC), length in terms of character and word count, repetitiveness in terms of text compression ratio, and readability in terms of the Simple Measure of Gobbledygook (SMOG). Extending datasets of music listening histories and genre annotations from Last.fm, we extract and analyze 424,476 song lyrics by 18,724 artists from LyricWiki.
We set out to answer whether there exist significant genre differences in song lyrics (RQ1) and artist Wikipedia articles (RQ2) in terms of repetitiveness and readability. We also assess whether we can find evidence to support the cliche´ that lyrics of very popular artists are particularly simple and repetitive (RQ3). We further investigate whether the characteristics of popularity, length, repetitiveness, and readability correlate within and between lyrics and Wikipedia articles (RQ4).
We identify substantial differences in repetitiveness and readability of lyrics between music genres. In contrast, no significant differences between genres are found for artists' Wikipedia pages. Also, we find that lyrics of highly popular artists are repetitive but not necessarily simple in terms of readability. Furthermore, we uncover weak correlations between length of lyrics and of Wikipedia pages of the same artist, weak correlations between lyrics' reading difficulty and their length, and moderate correlations between artists' popularity and length of their lyrics.
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Cross Ref
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- Markus Schedl. 2016. The LFM-1b Dataset for Music Retrieval and Recommendation. In Proceedings of the 6th ACM International Conference on Multimedia Retrieval (ICMR 2016). ACM, New York, NY, USA, 103-110. Google ScholarDigital Library
- Markus Schedl. 2017. Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset. International Journal of Multimedia Information Retrieval 6, 1(2017), 71-84.Google ScholarCross Ref
- Markus Schedl and Christine Bauer. 2017. Introducing Global and Regional Mainstreaminess for Improving Personalized Music Recommendation. In Proceedings of the 15th International Conference on Advances in Mobile Computing & Multimedia (MoMM 2017). ACM, Salzburg, Austria, 74-81. Google ScholarDigital Library
- Markus Schedl and Bruce Ferwerda. 2017. Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags. In Proceedings of the 2017 IEEE International Symposium on Multimedia (ISM). IEEE, Taichung, Taiwan, 479-482.Google ScholarCross Ref
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