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).

How will people sift and navigate information intelligently in the future, when there's even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up. Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Using this technique the team was able to generate a list of keywords their test readers mentally flagged as informative as they read -- which could then, for example, be used to predict other relevant Wikipedia articles to that person. Or, down the line, help filter a social media feed, or flag content that's of real-time interest to a user of augmented reality, for example.

How will people sift and navigate information intelligently in the future, when there's even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up. Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Using this technique the team was able to generate a list of keywords their test readers mentally flagged as informative as they read -- which could then, for example, be used to predict other relevant Wikipedia articles to that person. Or, down the line, help filter a social media feed, or flag content that's of real-time interest to a user of augmented reality, for example.