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.

社交媒体平台可以迅速向不同的受众传播 STEM 内容，但其运作方式可能令人费解。我们使用开源机器学习方法，如聚类、回归和情感分析，分析了来自 6 个社交媒体 STEM 创作者的 1000 多个视频及其指标。我们的数据提供了关于受众如何产生兴趣信号（点赞、收藏、评论、分享）的见解，不同信号与观看量之间的相关性，以及新创作者的内容如何被传播。我们还通过分析仅限内容创作者可获取的数据以及对评论进行情感分析，分享了优化传播的见解。