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Abstract
This study offers empirical evidence of Mix-of-Attributes (MOA) approach's analytical benefits, and illustrates how the MOA approach can be utilized. The study begins by content analyzing the most popular Web sites containing political user-generated content (UGC) and documenting presence of search efficiency, customizability, manipulability, participation cost reduction, and community orientation technological attributes. A cluster analysis is then used to develop classification of political UGC Web sites based on their attribute scores. The conventional and the attribute-based classifications of UGC are shown to be different, providing evidence of the MOA approach's usefulness. Theory-building implications of the attributes, the attribute-based classification, and the MOA approach are discussed.
Acknowledgment
The author would like to thank William P. Eveland, Jr., Kelly Garrett, and Michael McCluskey for their helpful insights and suggestions during the conceptual and operational stages of this research. The author would also like to thank Daniel Case and Ujala Rizwan Abbasi who helped with content coding.
Additional information
Notes on contributors
Ivan Dylko
Ivan Dylko (Ph.D., Ohio State University) is an assistant professor in Department of Communication Studies at New Mexico State University. His research interests include communication technology and political communication.