*Names in bold indicate Presenter
This study contributes to the literature by offering a first look into intra-government federal use of social media in the context of cross-agency communication. Using Twitter as the communication platform, the first objective is to discern who-follows-whom patterns that exist across agencies. The study will specifically focus on federal agencies that conduct and disseminate research. The second objective investigates the posting and sharing of other agencies’ posts, known as tweets and retweets, to determine levels of influence and potential for collaboration, taking into consideration an agency’s overall research mission and organizational characteristics (such as size). Understanding how agencies leverage Twitter to foster knowledge sharing through intra-government retweets motivates this research.
This study uses a combination of primary and secondary data. Focusing on Twitter feeds of 110 federal research-based departments, agencies, and affiliates, I collected 113,746 tweets with meta-data posted from January 2013 to March 2014. These cross-sectional data were supplemented with publicly available agency characteristics. Exponential random graph models are used to investigate intra-government federal followership and the propensity to form network ties. Network autoregressive outcome models are used to assess the propensity to retweet within and across federal institutions.
The findings of this study offer three-fold benefits. First, it contributes to the literature on government-to-public communication and knowledge management theory to practice, while assessing the efficacy of social media for transparency, and accountability under the Open Government Directive. Second, it applies empirical network analysis techniques in a novel way to research relationships within subsections of government, providing evidence of collaboration in new ways. Third, it presents new perspectives on how technology policies through social media may address government as a communicator of the research it generates.
- janzen_20141108_appam.pdf (1221.0KB)