Study title

This dataset compares different media sources reporting on violent events, using the 2017 Kenyan elections as a case study. It compares reports generated through traditional media, using the ACLED database (a much used source for comparing traditional media reports), to Twitter reports of violence, using a novel method for combing Twitter for violence related tweets, using an algorithm developed by the Department of Informatics at the University of Sussex (named Method 52), along three dimensions: 1) Geography and geographical coverage 2) Temporality- timeliness, temporal coverage and time precision and 3) Targeting/representativeness.The project will produce a robust evidence base on the opportunities and limitations of social media data on violence reporting to inform UK emergency and crisis response, in the context of violence monitoring in Kenya. Effective UK Government crisis and emergency response increasingly depends on the availability of timely, reliable data on political violence, to determine the scale and dimensions of crises and tailor responses. While social media reports of violence can inform the design, targeting, and geography of crisis response, there is limited robust research on their reliability and comprehensiveness. This project addresses this gap, by testing reliability and comprehensiveness of social media data, against conventional media reporting of violence in a real-time context: the August 2017 Kenyan elections. It will identify opportunities new data provide for policy, and what limitations restrict usability, along three dimensions: 1) reporting timeliness; 2) targeting of crisis response; and 3) geographies of violence risk. Building on extensive social media use in Kenya, and a history of violence reporting via social media the case facilitates a test of social media data in a promising context. The project is being carried out in partnership with researchers at the University of Sussex, the Armed Conflict Location & Event...