  Access to sign language data is far from adequate. We show that it is
possible to collect the data from social networking services such as TikTok,
Instagram, and YouTube by applying data filtering to enforce quality standards
and by discovering patterns in the filtered data, making it easier to analyse
and model. Using our data collection pipeline, we collect and examine the
interpretation of songs in both the American Sign Language (ASL) and the
Brazilian Sign Language (Libras). We explore their differences and similarities
by looking at the co-dependence of the orientation and location phonological
parameters
