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

摘要

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.;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.