ON THE ATTACK: U.S. GUBERNATORIAL CANDIDATE DIRECT CAMPAIGN DIALOGUE ON TWITTER

Abstract

Political candidates are increasingly utilizing social media in their campaign strategies. For example, of the 78 gubernatorial candidates in the 2014 U.S. elections, 76 actively tweeted messages in the months leading up to the election. In this paper we report on the initial stages of an ongoing project examining the messaging practices of these candidates on Twitter, Facebook and Instagram, as well as the discussions around the candidates. The work presented here focuses on the explicit referencing (@mentioning) practices on Twitter among the 2014 gubernatorial candidates.

Studies of election messaging online are certainly not new. Xenos and Foot (2005) provide an in-depth study of candidate messaging practices on Web pages during the 2002 U.S. election cycle and found that candidates communicated their position on issues far more frequently than they engaged in campaign issue dialogue. That is, candidates tended to avoid directly or indirectly mentioning their opponents, depriving voters of a clear understanding of where they stand. Stromer-Galley’s (2000) assessment of the 1996 presidential and 1998 gubernatorial campaigns found that candidates actively avoided on-line interaction with their opponents on their websites. More recent scholarship exploring politician’s messaging on Twitter (Golbeck, Grimes, & Rogers, 2010;; Hemphill, Otterbacher & Shapiro, 2013) finds that members of congress use social media as a broadcast mechanism, rather than as a mechanism for interaction with constituents. A common theme in these studies has been in determining if Internet technologies promote transparency and deliberation.

Our work differs from this stance in that we focus specifically on @mentioning behavior among our gubernatorial candidates on Twitter. We consider @mentioning a means to engage in a publicly visible conversation with a specific candidate. Thus, we conceptualize @mentions as a form of direct campaign dialogue (Xenos and Foot, 2005). Based on Xenos and Foot’s (2005) work, we expect our incumbent candidates to engage in direct campaign issue dialog less frequently than challengers. By examining messages utilizing machine learning trained with manually coded data, our intent is to provide insight into the differences in campaign dialogue practices among challengers and incumbents, democrats and republicans, as well as inter and intra-state.