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A Hyperlink and Sentiment Analysis of the 2016 Presidential Election: Intermedia Issue Agenda and Attribute Agenda Setting in Online Contexts

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A Hyperlink and Sentiment Analysis of the 2016 Presidential Election: Intermedia Issue Agenda and Attribute Agenda Setting in Online Contexts A HYPERLINK AND SENTIMENT ANALYSIS OF THE 2016 PRESIDENTIAL ELECTION: INTERMEDIA ISSUE AGENDA AND ATTRIBUTE AGENDA SETTING IN ONLINE CONTEXTS Youngnyo Joa A Dissertation Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY August 2017 Committee: Gi Woong Yun, Committee Co-Chair Kate Magsamen-Conrad, Committee Co-Chair Bill Albertini Graduate Faculty Representative Sung-Yeon Park © 2017 Youngnyo Joa All Rights Reserved iii ABSTRACT Gi Woong Yun, Committee Co-Chair Kate Magsamen-Conrad, Committee Co-Chair This study investigated the intermedia agenda-setting dynamics among various media Twitter accounts during the last seven weeks before the 2016 U.S. presidential election. Media Twitter accounts included in analysis were those of print media, television networks, news magazines, online partisan media, online non-partisan media, and political commentators. This study applied the intermedia agenda-setting theory as the theoretical framework, and network analysis and computer-assisted content analysis enabling hyperlink and sentiment analysis as the methods. A total of 5,595,373 relationships built via Tweets among media Twitter accounts was collected. After removal of irrelevant data, a total of 16,794 relationships were used for analysis. The results showed that traditional media Twitter accounts, such as print media and television networks, play roles in the Tweeting network by bridging isolated media Twitter accounts, and are located in the center of networks, so that information reaches them quickly; further, they are connected to other important accounts. Together with the changes in the volume of Tweeting that signaled media interest, the set of popular URLs and keywords/word pairs in Tweets also served as sensors that detected media Twitter accounts’ interest about that time. The results also supported the previous research findings that, as political events, the debates affect the production and dissemination patterns of news. Not only did the volume of Tweeting produced spiked immediately after each debate, but various types of hyperlinks and sentiment words used in Tweets increased as well. iv The number of negative sentiment words observed in the Tweeting network surpassed the number of positive sentiment words observed in the Tweeting network across different time points, and the gap between them decreased as the election approached. The use of positive and negative sentiment words differed across different media Twitter account categories. Online non-partisan media reported the highest use of positive sentiment words, while political commentators reported the highest level of negative sentiment word use. With respect to sentiment contagion, this study found the influence of online media and partisanship on intermedia agenda-setting dynamics within Twitter. Lastly, there were more evident individual agenda setters that affected negative sentiment contagion in multiple media categories, while in positive sentiment contagion, there was no distinctive media Twitter account found. The results advocated a multimethod approach to explore the dynamics of intermedia agenda-setting and sentiment contagion within Twitter. Limitations and future research were addressed as well. v ACKNOWLEDGMENTS I would like to express my special appreciation to my advisor Dr. Gi Woong Yun, for encouraging me to grow as a better researcher and person. I have always felt grateful to have him as my advisor, as he is someone who thrives on riding the scholarly wave and facilitating others to do so as well. I would also like to thank my co-advisor Dr. Kate Magsamen-Conrad. Being in her research team has been an amazing experience, her guidance and support helped me stay focused, especially during the tough times. I would also like to express my gratitude to my committee members Dr. Sung-Yeon Park and Dr. Bill Albertini. I am grateful that Dr. Park was willing to listen to me and offer help during my dissertation writing period as well as various phases of my graduate studies when I frequently stumbled. She has been an inspiring figure to me as a researcher, by being consistent and a caring colleague to others. I thank Dr. Albertini for being supportive and closely engaged with my dissertation project. His thoughtful comments and constructive suggestions helped me greatly improve my dissertation. I also want to extend my deepest thanks to my academic mentors in Seoul, Dr. Sooyoung Lee and Dr. Daiwon Hyun. The guidance and encouragement they have provided allowed me to begin, continue and finish this journey. I was fortunate to have such tremendous mentors who are passionate about what we do and believe in the changes that we can make. I will always be grateful and appreciative of the marks they left on both my academic journey and on the path of life. Thank you for always reminding me to stay positive and humble. I would like to acknowledge the support offered by my colleagues in the graduate program and my friends in Seoul who always made time for me and were there through the good times and the bad. To Kisun, thank you for our walks together, even when it was inconvenient. Lastly, this dissertation is dedicated to my parents who never stopped me from dreaming bigger and my brother who always encouraged me to be me. vi TABLE OF CONTENTS Page CHAPTER I. INTRODUCTION…………………………………………………………… 1 Agenda Setting in Online Contexts………………………………………………… 4 Intermedia Agenda Setting within Social Media…………………………… 6 Attribute Agenda on Twitter………………………………………………… 9 Network Analysis…………………………………………………………………… 11 Computer-Assisted Content Analysis……………………………………………… 12 Hyperlink Analysis…………………………………………………………. 12 Sentiment Analysis…………………………………………………………. 13 Time-Series Analysis………………………………………………………………. 14 Purposes of This Study……………………………………………………………… 15 Research Method…………………………………………………………………… 20 Organization of the Dissertation…………………………………………………… 21 CHAPTER II. LITERATURE REVIEW…………………………………………………... 22 Agenda-Setting in Online Contexts………………………………………………… 22 Intermedia Agenda-Setting on Twitter……………………………………………… 26 Social Media Effects…………………………………………………………. 30 Sentiment: the Agenda of Attributes………………………………………………… 33 Political Candidate Attributes ……………………………………………… 33 Attribute Dimensions……………………………………………………… 35 Sentiment in Online Contexts……………………………………………… 37 Network Analysis……………………………………………………………………. 39 vii The Concept of Network……………………………………………………. 40 Network Centrality…………………………………………………………... 41 Computer-Assisted Content Analysis………………………………………………. 43 Sentiment Analysis…………………………………………………………. 43 Agenda-Setting Examined by Sentiment Analysis…………………………… 46 Hyperlink Analysis in Agenda-Setting Studies……………………………… 50 The Structure of Hyperlinks in Twitter Feeds……………………………… 52 CHAPTER III. RESEARCH QUESTIONS AND HYPOTHESES………………………. 56 Twitter Network Change……………………………………………………………. 57 Cross-Linking Across Different Media Twitter Accounts…………………………... 60 Media Twitter Accounts’ Use of Sentiment………………………………………… 62 Key Media Twitter Accounts in Network…………………………………………… 64 The Temporal Dynamics of Sentiment……………………………………………... 65 Mapping Intermedia Agenda Setting Influence……………………………………… 69 CHAPTER IV. METHOD…………………………………………………………………… 71 Procedure……………………………………………………………….…………… 71 Sample………………………………………………………...……...……… 72 Data Acquisition……………………………………………………………. 74 Unit of Analysis……………………………………………….……………. 76 Data Analysis………………………………………………………………... 76 Message Level Content Analysis …………………………………... 77 Network Analysis …………………………………………………... 77 Hyperlink Analysis …………………………………………………... 77 viii Sentiment Analysis …………………………………………………... 78 Time-Series Analysis ………………………………………………... 78 Mapping Granger Causality Relationships ………………………… 80 Measurement………………………………………………………………………… 80 Types of Media Twitter Accounts…………………………………………… 80 Information in the Text Streams………………………...…….……………. 82 Hyperlink Salience…………………………………………………… 82 Sentiment Orientation and Salience ………………………………… 82 Network Centrality …………………………………………………… 82 Cues in the Tweeting Trends………………………………………………. 83 Political Events ……………………………………………………… 83 Media Interest ……………………………………………………… 83 Causal Relationship Between Two Time-Series Data Sets………………… 84 CHAPTER V. RESULTS…………………………………………………………………… 85 Descriptive Statistics………………………………………………………………… 85 Results at Time 1……………………………………………………………. 85 Results at Time 2……………………………………………………………. 92 Results at Time 3……………………………………………………………. 98 Results at Time 4……………………………………………………………. 104 Results at Time 5……………………………………………………………. 110 Results at Time 6……………………………………………………………. 116 Results at Time 7……………………………………………………………. 122 Results of Research Questions and Hypotheses……………………………………... 128 ix Analysis of Tweeting Network Change……………………………………… 128 The Debate Effects on News Tweets ……………………………… 128 Hyperlink Frequency on the Network ……………………………… 129 Sentiment Word Frequency on the Network ……………………… 131 Prevalent Sentiment on the Network ……………………………… 132 Time-Series of Three Indicators …………………………………… 134 Cross-Linking Across Different Media Twitter Accounts…………………… 136 Debate Effects on Cross-Linking Practices ………………………… 136 Crosslinking & Sentiment …………………………………………… 136 Media Twitter Accounts’ Use of Sentiment Words………………………… 139 Network Analysis of Media Twitter Accounts……………………………… 142 The Temporal Dynamics of Intermedia Agenda-Setting…………………… 150 Intermedia Agenda-Setting among Different Media Types ………… 150 Intermedia Agenda-Setting among Individual Media Twitter
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