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

Autor:	Oshinowo, Oluwamayokun, Delgado, Priscila, Fay, Meredith, Luna, C. Alessandra, Dissanayaka, Anjana, Jeltuhin, Rebecca, Myers, David R.
Rok vydání:	2024
Předmět:	Computer Science - Social and Information Networks Computer Science - Computers and Society Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 17 pages, 3 figures, 3 supplemental figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.18944 Zobrazit plný text záznamu View this record from Arxiv