Tweeting for the Cause: Network analysis of UK petition sharing

Online government petitions represent a new data-rich mode of political participation. This work examines the thus far understudied dynamics of sharing petitions on social media in order to garner signatures and, ultimately, a government response. Using 20 months of Twitter data comprising over 1 million tweets linking to a petition, we perform analyses of networks constructed of petitions and supporters on Twitter, revealing implicit social dynamics therein. We find that Twitter users do not exclusively share petitions on one issue nor do they share exclusively popular petitions. Among the over 240,000 Twitter users, we find latent support groups, with the most central users primarily being politically active ‘average’ individuals. Twitter as a platform for sharing government petitions, thus, appears to hold potential to foster the creation of and coordination among a new form of latent support interest groups online. I. Introduction: Internet-based platforms facilitate citizen participation in government, and government-sponsored petition websites—available in a number of democracies, including the United Kingdom and the United States—are an increasingly popular example. On such platforms, citizens undertake a ‘tiny act’ of political participation (Margetts et al., 2015), expressing their policy preferences to government and lobbying for change. Petitions have a rich history in the UK, and online petitions, in some ways, present a continuation of that tradition (Bochel 2012). Although petitions are not new, online petition platforms—together with social media—offer a means of circulating and tallying signatures on a scale never seen 1 Acknowledgment The data collection and preliminary analysis for this paper was carried out as part of the ESRC professorial fellowship, ‘The Internet, Political Science and Public Policy: Reexamining Collective Action, Governance and Citizen-Government Interactions in the Digital Era' RES-051-27-0331 (2011-2015), and we thank the ESRC for their support. We would also like to thank Alex Paseltiner, Reid Pryzant, Matt McNaughton, Matt Baya, and Williams College OIT for computational assistance during this project.