  Social media platforms can quickly disseminate STEM content to diverse
audiences, but their operation can be mysterious. We used open-source machine
learning methods such as clustering, regression, and sentiment analysis to
analyze over 1000 videos and metrics thereof from 6 social media STEM creators.
Our data provide insights into how audiences generate interest signals(likes,
bookmarks, comments, shares), on the correlation of various signals with views,
and suggest that content from newer creators is disseminated differently. We
also share insights on how to optimize dissemination by analyzing data
available exclusively to content creators as well as via sentiment analysis of
comments.
