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

TL;DR

This study uses machine learning techniques to analyze how STEM content spreads on social media, revealing audience engagement patterns and differences based on creator popularity, with insights for optimizing dissemination strategies.

Contribution

It introduces a data-driven approach using open-source ML tools to understand and improve the dissemination of STEM content on social media platforms.

Findings

Audience interest signals correlate with view counts.

Newer creators' content disseminates differently.

Sentiment analysis of comments offers insights for optimization.

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.