Investigating the dissemination of STEM content on social media with computational tools

These authors contributed equally to this work *Corresponding author. Abstract: 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. Introduction: Social media platforms such as Instagram, TikTok, and YouTube provide a new venue to promote STEM education, inspire the next generation of diverse scientists, and share knowledge to lower barriers to academia(1-3). Unlike many existing venues, social media is broadly accessible and not limited to those with significant resources devoted to their education. Content can be quickly disseminated to large diverse audiences of all ages and backgrounds(4).