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

Oluwamayokun Oshinowo, Priscila Delgado, Meredith Fay, C. Alessandra Luna, Anjana Dissanayaka +2 more
4/25/2024
cs.SIcs.CYcs.LG

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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.

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Cite this paper

@article{oshinowo2024investigating,
  title  = {Investigating the dissemination of STEM content on social media with computational tools},
  author = {Oluwamayokun Oshinowo and Priscila Delgado and Meredith Fay and C. Alessandra Luna and Anjana Dissanayaka and Rebecca Jeltuhin and David R. Myers},
  year   = {2024},
  eprint = {2404.18944},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.18944v1}
}

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