Investigating the dissemination of STEM content on social media with computational tools
This work addresses the dissemination of STEM content on social media for creators and researchers, but it is incremental as it applies existing computational tools to a specific domain without major methodological breakthroughs.
The study tackled the problem of understanding how STEM content spreads on social media by analyzing over 1000 videos from 6 creators using machine learning methods, finding that newer creators disseminate content differently and providing insights into optimizing dissemination based on audience signals and sentiment analysis.
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.