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.

翻译：社交媒体平台能够迅速将STEM内容传播给多元受众，但其运作机制仍带有神秘色彩。我们采用聚类分析、回归分析和情感分析等开源机器学习方法，对6位STEM创作者在社交媒体上发布的1000余个视频及其相关指标进行了分析。研究数据揭示了受众如何生成兴趣信号（点赞、收藏、评论、分享）、各类信号与观看量的相关性，并表明新创作者的内容传播呈现出差异化特征。我们还通过分析仅创作者可获取的数据以及对评论进行情感分析，分享了优化内容传播策略的见解。