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.

社交媒体平台可以迅速向不同的受众传播 STEM 内容，但其运作方式可能令人费解。我们使用开源机器学习方法，如聚类、回归和情感分析，分析了来自 6 个社交媒体 STEM 创作者的 1000 多个视频及其指标。我们的数据提供了关于受众如何产生兴趣信号（点赞、收藏、评论、分享）的见解，不同信号与观看量之间的相关性，以及新创作者的内容如何被传播。我们还通过分析仅限内容创作者可获取的数据以及对评论进行情感分析，分享了优化传播的见解。

利用计算工具调查社交媒体上 STEM 内容的传播

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

Data is always at the center of the theoretical development and investigation
of the applicability of formal concept analysis. It is therefore not surprising
that a large number of data sets are repeatedly used in scholarly articles and
software tools, acting as de facto standard data sets. However, the
distribution of the data sets poses a problem for the sustainable development
of the research field. There is a lack of a central location that provides and
describes FCA data sets and links them to already known analysis results. This
article analyses the current state of the dissemination of FCA data sets,
presents the requirements for a central FCA repository, and highlights the
challenges for this.

该研究分析了现有 FCA 数据集传播的情况，提出了建立一个中心 FCA 数据存储库的要求，并强调了此举面临的挑战。