Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/137000
Title: | Personality predicts words in favorite songs | Authors: | Qiu, Lin Chen, Jiayu Ramsay, Jonathan Lu, Jiahui |
Keywords: | Social sciences::Psychology | Issue Date: | 2018 | Source: | Qiu, L., Chen, J., Ramsay, J., & Lu, J. (2019). Personality predicts words in favorite songs. Journal of Research in Personality, 78, 25-35. doi:10.1016/j.jrp.2018.11.004 | Journal: | Journal of Research in Personality | Abstract: | Psychologists have long theorized that people actively create, select, or modify experiences and situations to fulfill their individual psychological needs. However, little is known about how people may use forms of art and entertainment such as music to enhance their experiences and shape their environments for need satisfaction. In this research, we measured participants’ personality and the linguistic styles of their favorite songs, and observed significant associations between personality traits and linguistic cues in lyrics. These associations were stronger for participants who generally liked a song because of its lyrics rather than melody. Our study is the first to show how one's personality is related to linguistic cues in someone else's writings. It points to the possibility that people may like certain songs because the linguistic cues in the lyrics are congruent with their personality and hence can satisfy personal needs. This expands research on person-situation interaction and literature on personality and language use, and has important practical implications. | URI: | https://hdl.handle.net/10356/137000 | ISSN: | 0092-6566 | DOI: | 10.1016/j.jrp.2018.11.004 | Rights: | © 2018 Elsevier Inc. All rights reserved. This paper was published in Journal of Research in Personality and is made available with permission of Elsevier Inc. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SSS Journal Articles |
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Personality predicts words in favorite songs.pdf | 890.24 kB | Adobe PDF | ![]() View/Open |
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