Music listening is a significant part of average teenagers' life, creating spaces around them, changing scenery and moods, being involved in their socialization and shaping and presenting one's identity. "Musical nutrition" (MN) is the complexity and diversity of musical attributes and characteristics, which construct the listener's frequent playlist. This study explores the elements that affect teenagers' MN, focusing on their musical mentors (patents, teachers, friends and algorithms), their preferred surroundings, means of listening and the effects of Covid-19 lockdowns on those. Participants (38) are divided: half with musical background and half without. The study adopts a mixed-method, combining interviews with questionnaires, playlist reports and musical analysis of these reports. Preliminary results show higher MN among participants with musical background and among those who have multiple musical mentors. The study presents the term "musical nutrition", an innovative approach to data collection and musical analysis, using an original scale, and it is the first to compare the influences of human vs algorithmic mentors on musical preferences. little research has been done on Teenagers' listening habits, particularly in Israel (see: Katz-Gerro, 2003; Ratner, 2015), and was dealing mainly with general taste. Moreover, the current pandemic has affected music consumption in multiple ways. My research focuses on how Israeli teenagers maintain routine and human contacts by using music. Some use it as a coping tool, assisting with stress and recovery from illness (Mastnak, 2020). However, listening to music is associated with mobility and freedom (Low & Smart, 2020; Lehman, 2020), which was restricted in Israel and worldwide. The vast array of musical streaming platforms has enabled the rise of laypeople’s recommendations and of algorithmic ones, contributing, in the process, to the decline of influential figures such as radio editors, MTV, VH-1 and music stores salespeople (Regev, 2011). Furthermore, it creates an emotional connection with – and even dependence on – music technologies (Nowak, 2014, 2016). If we are becoming an algorithmic society and algorithms create most human connections (Striphas, 2015; Seaver, 2018), the impact of actual human ones should be explored, as well as the balance between them and algorithmic influences. I believe that music education in our era could benefit from exploration of agents', particularly teachers', function as mentors, vs algorithmic influences on teenage consumers. Crossing data from musical analysis of playlists with information compiled from interviews about preferences and influences might point us towards that goal.

Katz-Gerro, T. (2003). Consumption-Based Inequality: Household Expenditures and Possession of Goods in Israel, 1986–1998. In Transitions in Domestic Consumption and Family Life in the Modern Middle East: Houses in Motion (pp. 167-189). New York: Palgrave Macmillan. Lehman, E. T. (2020). “Washing Hands, Reaching Out”–Popular Music, Digital Leisure and Touch during the COVID-19 Pandemic. Leisure Sciences, 1-7. Low, S., & Smart, A. (2020). Thoughts about Public Space During Covid‐19 Pandemic. City & Society (Washington, DC), 32(1). Mastnak, W. (2020). Psychopathological problems related to the COVID‐19 pandemic and possible prevention with music therapy. Acta Paediatrica. (September 2020) from https://onlinelibrary.wiley.com/doi/full/10.1111/apa.15346 Nowak, R. (2014). Investigating the interactions between individuals and music technologies within contemporary modes of music consumption. First Monday, 19(10). Nowak, R. (2016). Consuming music in the digital age: Technologies, roles and everyday life. New York: Palgrave Macmillan. Ratner, D. (2015). Rap, reggae, and the “Strolles”: Musical tastes and identities among Israeli-Ethiopian youth. Israeli Sociology, 17(1), 32-56. Regev, M. (2011). The Sociology of Culture: Introduction. The Open University of Israel. Seaver, N. (2018). What should an anthropology of algorithms do?. Cultural anthropology, 33(3), 375-385. Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(4- 5), 395-412.