Tag2Risk: Harnessing Social Music Tags for Characterizing Depression Risk

Autor:	Surana, Aayush, Goyal, Yash, Shrivastava, Manish, Saarikallio, Suvi, Alluri, Vinoo
Rok vydání:	2020
Předmět:	Computer Science - Information Retrieval Computer Science - Multimedia Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	Musical preferences have been considered a mirror of the self. In this age of Big Data, online music streaming services allow us to capture ecologically valid music listening behavior and provide a rich source of information to identify several user-specific aspects. Studies have shown musical engagement to be an indirect representation of internal states including internalized symptomatology and depression. The current study aims at unearthing patterns and trends in the individuals at risk for depression as it manifests in naturally occurring music listening behavior. Mental well-being scores, musical engagement measures, and listening histories of Last.fm users (N=541) were acquired. Social tags associated with each listener's most popular tracks were analyzed to unearth the mood/emotions and genres associated with the users. Results revealed that social tags prevalent in the users at risk for depression were predominantly related to emotions depicting Sadness associated with genre tags representing neo-psychedelic-, avant garde-, dream-pop. This study will open up avenues for an MIR-based approach to characterizing and predicting risk for depression which can be helpful in early detection and additionally provide bases for designing music recommendations accordingly. Comment: Appearing in the proceedings of ISMIR 2020. Aayush Surana and Yash Goyal contributed equally
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.13159 Zobrazit plný text záznamu View this record from Arxiv