Unmasking Machine Learning With Tensor Decomposition: An Illustrative Example for Media and Communication Researchers
Yu Won Oh and
Chong Hyun Park
Additional contact information
Yu Won Oh: School of Digital Media, Myongji University, Republic of Korea
Chong Hyun Park: School of Business, Sungkyunkwan University, Republic of Korea
Media and Communication, 2025, vol. 13
Abstract:
As online communication data continues to grow, manual content analysis, which is frequently employed in media studies within the social sciences, faces challenges in terms of scalability, efficiency, and coding scope. Automated machine learning can address these issues, but it often functions as a black box, offering little insight into the features driving its predictions. This lack of interpretability limits its application in advancing social science communication research and fostering practical outcomes. Here, explainable AI offers a solution that balances high prediction accuracy with interpretability. However, its adoption in social science communication studies remains limited. This study illustrates tensor decomposition—specifically, PARAFAC2—for media scholars as an interpretable machine learning method for analyzing high-dimensional communication data. By transforming complex datasets into simpler components, tensor decomposition reveals the nuanced relationships among linguistic features. Using a labeled spam review dataset as an illustrative example, this study demonstrates how the proposed approach uncovers patterns overlooked by traditional methods and enhances insights into language use. This framework bridges the gap between accuracy and explainability, offering a robust tool for future social science communication research.
Keywords: automated content analysis; explainable AI; machine learning; PARAFAC2; tensor decomposition (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.cogitatiopress.com/mediaandcommunication/article/view/9623 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:cog:meanco:v13:y:2025:a:9623
DOI: 10.17645/mac.9623
Access Statistics for this article
Media and Communication is currently edited by Raquel Silva
More articles in Media and Communication from Cogitatio Press
Bibliographic data for series maintained by António Vieira () and IT Department ().