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Explication of Political User-Generated Content And Explication of Political User-Generated Content and Theorizing about Its Effects on Democracy with a Mix-of-Attributes Approach and Documenting Attribute Presence with a Quantitative Content Analysis DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Ivan B. Dylko Graduate Program in Communication The Ohio State University 2011 Dissertation Committee: Professor William Eveland, Advisor Professor Kelly Garrett Professor Michael McCluskey Copyrighted by Ivan B. Dylko 2011 Abstract The present study attempts to stimulate a new program of communication-effects investigations needed to catch up with the significant recent technological transformations in the communication environment. Eveland’s (2003) mix-of-attributes (MOA) approach is argued to represent a needed adjustment to how communication field investigates media effects in today’s communication environment. The MOA approach is used to explicate political user-generated content (UGC) and its five technological attributes. In this study, the most popular political UGC and traditional media Web sites are content analyzed to obtain a detailed description of the attribute presence across prominent groups of UGC Web sites, and to compare presence of attributes on UGC versus traditional news Web sites. Cluster analysis is used to develop a theoretically- and empirically-grounded classification of political UGC. Despite relatively low presence of attributes across different UGC Web sites, the study confirms usefulness of the MOA framework. Presence of attributes on traditional news Web sites suggests that the theoretical importance of the attributes might increase over time. This study advances communication-effects theory by: (1) examining the nature and potential effects of political UGC; and by (2) illustrating how MOA approach can be applied, given its strengths and weaknesses. Additional implications of results, study limitations, and directions for future research are discussed. ii Dedicated to my parents iii Acknowledgments I want to thank my advisor, William Eveland, for his wise guidance, extensive support, and patience during the work on this project. I want to thank my committee members, Kelly Garrett and Michael McCluskey, for their encouragement and numerous useful recommendations that helped improve this work. I am thankful to the members of the Communication, Opinion and Political Studies group in the School of Communication for providing thorough and always helpful critique of many developing ideas related to this project. Finally, I want to thank several dedicated undergraduate research assistants who helped improve the codebook and helped code part of the data used in this research. iv Vita 1999 ..................................................................... Gymnasia N2 2005 ..................................................................... B.A. Communication, Alcorn State University 2007 ..................................................................... M.A. Communication, Ohio State University 2008 to present .................................................... Graduate Teaching Associate, School of Communication, The Ohio State University Publications Dylko, I. B. (2010). An examination of methodological and theoretical problems arising from the use of political participation indexes in political communication research. International Journal of Public Opinion Research, 22(4), 523-534. Eveland, W. P., Jr., & Dylko, I. B. (2007). Reading political blogs during the 2004 election campaign: Correlates and consequences. In M. Tremayne (Ed.), Blogging, citizenship and the future of media. New York: Routledge. Fields of Study Major Field: Communication v Table of Contents Abstract……………………………………………………………………….……………………ii Acknowledgments……………………………………………………….……………………..…iv Vita…………………………………………………..…………….……………………….………v List of Tables…………………….………………..……………………………………....………ix List of Figures……………………………………..…………………………….…………………x Chapter 1: Introduction…………..………………..…………………………….…………………1 Chapter 2: Theorizing Challenges in Today’s Communication Environment….………………….3 Chapter 3: User-Generated Content.….…………..…………………………….………………...12 Chapter 4: Mix-of-Attributes (MOA) Approach.....…………………………….………………..24 Chapter 5: Using the MOA to Explicate Political UGC Attributes………………………………31 Political Participation………………...………………………………………………………31 Participatory Democracy Theory………………...………………………………………32 Variables and Processes Affecting Political Participation……….……………………....41 Technology………………...…………………………………………………………………44 Technological Attributes of UGC and Their Hypothesized Influence on Political Participation………………..…………………………………………………………………46 Task-Specific Information Retrieval Performance……..………...……………………...47 Information Environment Customizability……………...…...………...………………...53 Content Manipulability………………...................……………………………………...62 Direct Participation Facilitation………………...……………..…………………………65 vi Community Orientation……………...……………………..……………………………68 Chapter 6: Hypotheses and Research Question …..…………………………….………………..72 Chapter 7: Method…………………….…………..…………………………….………………..79 Sampling……………………….….…………..…………………………….………………..79 Blogs……………………….….…………..…………………………….……………….86 Wikis……………………….….…………..…………………………….……………….87 Content-Sharing Web Sites……………………….….…...…………….………………..87 Participatory Journalism Web Sites……………………….…………….……………….88 Discussion Forums…………………….………….….…...…………….………………..88 Reference Group………………………………….….…...…………….………………..89 Variables and Codebook…………………………….….….....…………….………………...89 Improving Comparability across Web Sites……………..………………….……………..…89 General Procedures for Identifying and Saving Content.……………….……………….89 Specific Procedures for Identifying and Saving Content.………..……..………………..90 Coders and Reliability Assessment.…………………………………..…….………………..94 Chapter 8: Results.…………………….…………..…………………………….………………..96 Hypothesis 1.……………………………………….……………..…….………………103 Research Question 1.……………………………….……………..…….……………...108 Hypothesis 2.……………………………………….……………..…….………………130 Hypothesis 3.……………………………………….……………..…….………………130 Chapter 9: Discussion.…………………..…………..…………………………….…………….131 Unexpected Findings……………...…………..…………………………….………………132 Hypothesis 3.……………………………………….……………..…….………………132 Hypothesis 1.……………………………………….……………..…….………………135 vii Consistently Low Scores on Attributes.…………………………..…….………………138 Presence of Attributes across UGC Web Sites.…………………………..…………………142 Limitations………………………...…………..…………………………….………………149 Implications..……………………...…………..…………………………….………………157 Low Attribute Presence..……………………………………………......………………157 Web Site Traffic Correlates with Attribute Presence.…………………..……………...157 Attribute Presence on UGC versus Traditional News Web Sites……….……………...158 Quantitative Rather than Qualitative Difference..…………..…………..……………...158 Conventional and New UGC Classification..………………………….…..…………...159 Theoretical Value of Explicated Attributes……………………………………….……162 Future Research..……………………………………………………………………………165 References……………………….………………..…………………………….……………….176 Appendix A: Codebook……………………...………………………………….………………191 viii List of Tables Table 1. Web Sites in the Sample…………………………………………………….…………..80 Table 2. Descriptive Characteristics for Attribute Scores on UGC versus Reference Group Web Sites …………………..……………………………….…..……………………………………...97 Table 3. Median Scores of Each Web Site Group on Attributes…………...….…..……………105 Table 4. Descriptive Characteristics of Data Outliers Replaced in Cluster Analysis...…………112 Table 5. Cluster Analysis Procedure Validity Assessment: Comparison of Two Split-Sample Cluster Solutions.……..………...…………….……………………….…..…………………….116 Table 6. Descriptive Characteristics for Attribute Scores.……………...………………………121 Table 7. Median Scores of Web Site Clusters on Attributes.……..………...……..……………125 Table 8. Post-Hoc Tests of Difference among Conventional UGC Groups on Attribute Scores.144 ix List of Figures Figure 1. Four Groups of Technological Features Representing Information Environment Customizability……….…………………………………………..………………………………55 Figure 2. Frequency Distributions of Attribute Scores……………………………..…………...100 Figure 3. Error Bars Plotting Rescaled Attribute Scores in Different Web Site Groups...……...106 Figure 4. Cluster Analysis Dendrograms: Comparison of Two Split-Sample Cluster Solutions.114 Figure 5. Cluster Analysis Dendrogram Displaying Web Sites Grouped According to Their Scores on Attributes…..…………………………………………………………………………117 Figure 6. Rescaled Frequency Distributions of Attribute Scores in Clusters…..……………….123 Figure 7. Error Bars Plotting Rescaled Attribute Scores by Cluster and Web Site Group ……..126 Figure 8. Distribution of Web Site Groups across Three Clusters……………...………………129 x Chapter 1: Introduction The present research is an attempt to stimulate a new program of communication-effects investigations that are argued to be superior to the conventional investigations in terms of theoretical nuance and long-term theoretical relevance. The primary impetus for this work is a realization by various communication-effects researchers that the field, which has progressed quite successfully in the past, has recently lost its theoretical vigor, in large part due to significant transformations in the communication environment that were unmatched by the necessary transformations in the communication field’s methodology and theory (Bennett & Iyengar, 2008; Chaffee & Metzger, 2001; Eveland, 2003). The historical context of the field’s development
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