YouTube and Science: Models for Research Impact
Abdul Rahman Shaikh, Hamed Alhoori, Maoyuan Sun

TL;DR
This paper investigates the relationship between research articles and YouTube videos, creating datasets and machine learning models to predict citations and popularity, revealing factors that influence research dissemination and engagement.
Contribution
The study introduces new datasets linking research articles and YouTube videos, and develops models to predict citations and popularity, advancing understanding of science communication online.
Findings
Most cited articles are in medicine and biochemistry.
Models achieved F1 scores between 80% and 94%.
Tweets and news coverage increase likelihood of video citations.
Abstract
Video communication has been rapidly increasing over the past decade, with YouTube providing a medium where users can post, discover, share, and react to videos. There has also been an increase in the number of videos citing research articles, especially since it has become relatively commonplace for academic conferences to require video submissions. However, the relationship between research articles and YouTube videos is not clear, and the purpose of the present paper is to address this issue. We created new datasets using YouTube videos and mentions of research articles on various online platforms. We found that most of the articles cited in the videos are related to medicine and biochemistry. We analyzed these datasets through statistical techniques and visualization, and built machine learning models to predict (1) whether a research article is cited in videos, (2) whether a…
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Taxonomy
TopicsMisinformation and Its Impacts · Advanced Text Analysis Techniques · Computational and Text Analysis Methods
