# YouTube and Science: Models for Research Impact

arXiv: https://arxiv.org/abs/2209.02380 · HTML: https://mlsanity.com/paper/2209.02380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics](https://mlsanity.com/paper/1406.7091/) (2014)
- [Predicting popularity of online videos using Support Vector Regression](https://mlsanity.com/paper/1510.06223/) (2015)
- [Measuring the Diversity of Facebook Reactions to Research](https://mlsanity.com/paper/2001.01029/) (2020)
- [Could scientists use Altmetric.com scores to predict longer term citation counts?](https://mlsanity.com/paper/1801.10311/) (2018)
- [Pandemics are catalysts of scientific novelty: Evidence from COVID-19](https://mlsanity.com/paper/2009.12500/) (2020)
- [Early Indicators of Scientific Impact: Predicting Citations with Altmetrics](https://mlsanity.com/paper/2012.13599/) (2020)
- [Shared Feelings: Understanding Facebook Reactions to Scholarly Articles](https://mlsanity.com/paper/1905.10975/) (2019)
- [Scholarly use of social media and altmetrics: a review of the literature](https://mlsanity.com/paper/1608.08112/) (2016)
- [The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity](https://mlsanity.com/paper/1311.6526/) (2013)
- [Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK Research Excellence Framework (REF)](https://mlsanity.com/paper/1807.03977/) (2018)
- [Predicting Patent Citations to measure Economic Impact of Scholarly Research](https://mlsanity.com/paper/1906.08244/) (2019)
- [Bibliometrics for Internet Media: Applying the h-Index to YouTube](https://mlsanity.com/paper/1303.0766/) (2013)
- [Holding a Conference Online and Live due to COVID-19](https://mlsanity.com/paper/2004.07668/) (2020)
- [SightBi: Exploring Cross-View Data Relationships with Biclusters](https://mlsanity.com/paper/2108.01044/) (2021)
- [Toward Systematic Design Considerations of Organizing Multiple Views](https://mlsanity.com/paper/2207.07558/) (2022)

## Built on by (most cited first, 1 of 1)

- [Public interest in science or bots? Selective amplification of scientific articles on Twitter](https://mlsanity.com/paper/2410.01842/) (2024)

## Abstract (arXiv)

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 research article cited in a video achieves a level of popularity, and (3) whether a video citing a research article becomes popular. The best models achieved F1 scores between 80% and 94%. According to our results, research articles mentioned in more tweets and news coverage have a higher chance of receiving video citations. We also found that video views are important for predicting citations and increasing research articles' popularity and public engagement with science.