Digital Politics Research Group
The Digital Politics Research Group (DPRG) comprises a group of members of staff and research students researching the intersection of the digital and the political. The members of staff in the group are: Dr. Matthew Wall (Political and Cultural Studies), Dr. Stephen Lindsay (Computer Science), Dr. Yan Wu (Languages, Translation and Communication), Dr. Nina Wiesehomeier (Political and Cultural Studies) and Dr. Daniel Archambault (Computer Science). Research students include Bibobra Aganaba and Martin Horton-Eddison.
The Group aims to achieve the following four objectives:
- Research outputs from key participants with leading book publishers and leading ISI-listed journals.
- A major contribution to the Political and Cultural Studies (PCS) and Computer Science (CS) REF2020 submission in terms of externally-funded, high impact research.
- A significant record of national and international collaborative research, including inter-disciplinary projects (indeed, developing genuine interdisciplinarity among group members is, in itself, a key objective of the centre).
- A focal point for the maintenance of research excellence among all staff and for Early Career researchers to develop research careers in the most supportive research environment.
Digital Citizenship and Engagement is the overarching theme that captures the core research focus of this group. It breaks down into the following three sub-categories.
- The politicisation of the internet. How do parties, interest groups and citizens use digital technology to influence politics?
- Citizen engagement through digital technologies – digital technologies present new opportunities for political engagement but also open new avenues for exclusion that are poorly understood but of interest to both human-computer interaction specialists and political scientists
- Digital data: the collection, analysis and visualisation of data in a digital environment – the data which can be gathered from digital sources includes qualitative and quantitative sources that require new forms of statistical and textual analysis