Using machine learning techniques to investigate learner engagement with TikTok media literacy campaigns

Abstract

Social media has the unique capacity to expose many learners to media literacy instruction via targeted campaigns. Investigating learner engagement and reaction to these efforts may be a fruitful endeavor for researchers that can inform the design of future campaigns. However, the massive datasets associated with social media posts are difficult, and often impossible, to analyze with traditional qualitative methods. This study seeks to address this problem by leveraging machine learning techniques to collect and analyze Big Data from two different media literacy campaigns on the youth-oriented social media platform TikTok. Specifically, we explore the ways topic modeling, sentiment analysis, and network analysis can provide insight into learner engagement with these campaigns and discuss limitations and implications for stakeholders interested in utilizing these approaches.

Keywords:

• Media literacy education
• artificial intelligence
• machine learning
• social media
• learner engagement

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 An example of a video from the “Media Literacy Week” campaign is linked here: https://www.tiktok.com/@tiktoktips/video/7068812229059267886?is_copy_url=1&is_from_webapp=v1&lang=en

2 An example of a video from the “Be Informed” campaign is linked here: https://www.tiktok.com/@tiktoktips/video/6854302292800179461

3 While we removed emojis and stopwords from the data set for analysis, we included them back in the findings section for readability.

4 Ibid.

Additional information

Notes on contributors

Christine Wusylko

Christine Wusylko is a PhD candidate at the University of Florida’s College of Education. Her research interests include STEM education and online information and algorithmic literacy.

Lauren Weisberg

Lauren Weisberg is a Ph.D. candidate at the University of Florida specializing in Curriculum and Instruction. Her research agenda is focused on innovative and equity-centered pedagogy as well as critically analyzing technology’s roles in teaching and learning.

Raymond A. Opoku

Raymond A. Opoku is a Ph.D. student at the UF College of Education with a research interest in AI in education particularly algorithmic biases in learning management systems. He is also a Data Engineer at the UF College of Medicine.

Brian Abramowitz

Brian Abramowitz is a PhD student at the University of Florida’s College of Education. His research interests are centered around the implementation of educational technologies as well as the misconceptions in the K-12 classroom.

Jessica Williams

Jessica Williams is a doctoral candidate in the Special Education program at the University of Florida. Her work focuses on inclusive, collaborative teacher education and professional development aimed at supporting diverse student populations through evidence-based instruction.

Wanli Xing

Wanli Xing is an Assistant Professor at the University of Florida’s College of Education. His research focuses on how emerging technologies can deeply transform STEM education and online learning.

Teresa Vu

Teresa Vu is an undergraduate student studying computer science at the University of Florida.

Michelle Vu

Michelle Vu is an undergraduate student studying computer science at the University of Florida.