Recommender systems on popular online platforms expose impressionable and easily influenced younger listeners to varied content, making it crucial to reflect on the songs children can encounter due to their interactions with recommender systems. To set a foundation, we analyze the lyrics of a catalog comprised of \(\sim 30,000\) songs to gauge their suitability to children. Our multi-perspective exploration reveals a high prevalence of inappropriate lyrics in music commonly heard by children. This highlights the need for further explorations pertaining online platforms and their recommender systems that curate and ultimately present items from catalogs such as the ones we examined, highlighting the potential negative impact of such lyrics on their behavior and personality by promoting harmful language or biases. Informed by our findings, we outline research directions for the information retrieval community to consider when designing, evaluating, and deploying algorithms that serve diverse audiences.