ABSTRACT
Political campaigns mostly run parallel to each other during an election cycle, but intersect when the main candidates face off for televised debates. They offer supporters of these candidates a chance to engage with each other while being exposed to views and opinions different from their own. This study uses a combination of social network analysis and machine learning to examine how the three US presidential debates of 2016 were live tweeted (N = ∼300,000). We find that despite cross-cutting exposure across the ideological divide, people remain highly partisan in terms of who they engage with on Twitter. The issue agendas of Twitter posts during the US presidential debates is set well in advance of the debates themselves; it is highly negative and focused on personality traits of the opposition candidate rather than policy matters. We also detect a shift in the nature of online opinion leadership, with grassroots activists and internet personalities sharing the space with traditional elites such as political leaders and journalists. This shift coincides with the broader anti-establishment turn in the US political climate, as reflected in the early success of Bernie Sanders and the eventual victory of a political outsider like Donald Trump over the seasoned Hillary Clinton.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Pei Zheng is the Assistant Professor at Roy H Park School of Communication, Ithaca College. Her research focuses on social impact of technology, and how media representation affect audience's perception and behavior. She uses quantitative and computational methods to study them. [email: [email protected]].
Saif Shahin is an Assistant Professor at School of Communication, American University. His research focues on critical data studies, social media studies, global media and politics. [email: [email protected]].