The UK Government's Hate Crime Action Plan (Home Office 2016) stresses the need to tackle hate speech on social media by bringing together policymakers with academics to improve the analysis and understanding of the patterns and drivers of cyberhate and how these can be addressed. Furthermore, the recent Home Affairs Select Committee Inquiry (2016) 'Hate Crime and its Violent Consequences' highlighted the role of social media in the propagation of hate speech (on which the proposers were invited to provide evidence). This proposal acknowledges the migration of hate to social media is non-trivial, and that empirically we know very little about the utility of Web based forms data for measuring online hate speech and counter hate speech at scale and in real-time. This became particularly apparent following the referendum on the UK's future in the European Union, where an inability to classify and monitor hate speech and counter speech on social media in near-real-time and at scale hindered the use of these new forms of data in policy decision making in the area of hate crime. It was months later that small-scale grey literature emerged providing a 'snap-shot' of the problem (Awan & Zempi 2016, Miller et al. 2016). In partnership with the UK Head of the Cross-Government Hate Crime Programme at the Department for Communities and Local Government (DCLG), and the London Mayor's Office for Policing and Crime's (MOPAC) new Online Hate Crime Hub, the proposed project will co-produce evidence on how social media data, harnessed by new Social Data Science methods and scalable infrastructure, can inform policy decision making. We will achieve this by taking the social media reaction to the referendum on the UK's future in the European Union as a demonstration study, and will co-develop with the Policy CI transformational New Forms of Data Capability contributions including: (i) semi-automated methods that monitor the production and spread of cyberhate around the case study and beyond; (ii) complementary methods to study and test the effectiveness of counter speech in reducing the propagation of cyberhate, and (iii) a technical system that can support real time analysis of hate and counter speech on social media at scale following 'trigger events', integrated into existing policy evidence-based decision-making processes. The system, by estimating the propagation of cyberhate interactions within social media using machine learning techniques and statistical models, will assist policymakers in identifying areas that require policy attention and better targeted interventions in the field of online hate and antagonistic content.