Social media professionals are shaping American political strategies
This article offers the first analysis of the role that technology companies, specifically Facebook, Twitter, Microsoft, and Google, play in shaping the political communication of electoral campaigns in the United States. We offer an empirical analysis of the work technology firms do around electoral politics through interviews with staffers at these firms and digital and social media directors of 2016 U.S. presidential primary and general election campaigns, in addition to field observations at the 2016 Democratic National Convention. We find that technology firms are motivated to work in the political space for marketing, advertising revenue, and relationship-building in the service of lobbying efforts. To facilitate this, these firms have developed organizational structures and staffing patterns that accord with the partisan nature of American politics. Furthermore, Facebook, Twitter, and Google go beyond promoting their services and facilitating digital advertising buys, actively shaping campaign communication through their close collaboration with political staffers. We show how representatives at these firms serve as quasi-digital consultants to campaigns, shaping digital strategy, content, and execution. Given this, we argue that political communication scholars need to consider social media firms as more active agents in political processes than previously appreciated in the literature.
The more expensive and competitive the election, the more Twitter will talk about it
In recent years, journalists, political elites, and the public have used Twitter as an indicator of political trends. Given this usage, what effect do campaign activities have on Twitter discourse? What effect does that discourse have on electoral outcomes? We posit that Twitter can be understood as a tool for and an object of political communication, especially during elections. This study positions Twitter volume as an outcome of other electoral antecedents and then assesses its relevance in election campaigns. Using a data set of more than 3 million tweets about 2014 U.S. Senate candidates from three distinct groups—news media, political actors, and the public—we find that competitiveness and money spent in the race were the main predictors of volume of Twitter discourse, and the impact of competitiveness of the race was stronger for tweets coming from the media when compared to the other groups. Twitter volume did not predict vote share for any of the 35 races studied. Our findings suggest that Twitter is better understood as a tool for political communication, and its usage may be predicted by money spent and race characteristics. As an object, Twitter use has limited power to predict electoral outcomes.
People are more interested in women candidates when they're running against men
As campaign discussions increasingly circulate within social media, it is important to understand the characteristics of these conversations. Specifically, we ask whether well-documented patterns of gendered bias against women candidates persist in socially networked political discussions. Theorizing power dynamics as relational, we use dialectic configurations between actors as independent variables determining network measures as outcomes. Our goal is to assess relational power granted to candidates through Twitter conversations about them and whether they change depending on the gender of their opponent. Based on more than a quarter of a million tweets about 50 candidates for state-wide offices during the 2014 US elections, results suggest that when a woman opposes a man, the conversation revolves around her, but she retains a smaller portion of rhetorical share. We find that gender affects network structure—women candidates are both more central and more replied to when they run against men. Despite the potential for social media to disrupt deeply rooted gender bias, our findings suggest that the structure of networked discussions about male and female candidates still results in a differential distribution of relational power.