Media Literacy Education on TikTok: Investigating Learner Engagement Using Machine Learning

• In Event: Friday Roundtable Session 4:55 pm
  In Roundtable Session: Digital Tools and Platforms in Learning and Education (Table 7)

Abstract

Social media can expose a large number of learners to media literacy instruction via targeted campaigns. Investigating learner engagement and reaction to these efforts may be a fruitful endeavor for researchers to inform the design of future campaigns. However, the massive datasets associated with social media are difficult, or 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.

Authors

• Christine Wusylko, Kennesaw State University

• Lauren Weisberg, University of Texas - Arlington

• Raymond Opoku, University of Florida

• Brian Abramowitz, University of Florida

• Jessica Williams, University of Florida

• Wanli Xing, University of Florida

• Teresa Vu, University of Florida

• Michelle Vu, University of Florida